.

Highway Net^{[HW1]} with open gates 
LBH also have participated in other PR work that has misled many. For example, the narrator of a popular 2018 Bloomberg video^{[VID2]} is thanking Hinton for speech recognition and machine translation, although both were actually done (at production time of the video) on billions of smartphones by deep learning methods developed in my labs in Germany and Switzerland (LSTM & CTC) long before Hinton's less useful and more traditional methods (see dispute H5). Similarly, in 2016, the NY Times published an article^{[NYT3]} about the new, greatly improved, LSTMbased Google Translate without even mentioning our LSTM (instead featuring Hinton who had little to do with it), although Google's original 2016 paper on Google Translate^{[WU]} mentions LSTM over 50 times.
In 2022, LeCun listed the 5 best ideas 20122022 without mentioning that most of them are from my lab, and much older. See Addendum II of [LEC] and this tweet (22 Nov 2022).
LeCun also claimed about me:^{[LEC22c]} "... there's a big difference between just having the idea, and then getting it to work on a toy problem, and then getting it to work on a real problem, and then doing a theory that shows why it works, and then deploying it. There's a whole chain, and his idea of scientific credit is that it's the very first person who just, sortof, you know, had the idea of that, that should get all the credit. And that's ridiculous."
In no universe is this straw man argument true.^{[LEC]} As I wrote in a previous critique (one which LBH know well):^{[DLC]} "the inventor of an important method should get credit for inventing it. She may not always be the one who popularizes it. Then the popularizer should get credit for popularizing it (but not for inventing it)." Nothing more or less than the standard elementary principles of scientific credit assignment.^{[T22]} LBH, however, apparently aren't satisfied with credit for popularising the inventions of others; they also want the inventor's credit.^{[LEC]}
★1. LBH claim to "briefly describe the origins of deep learning"^{[DL3a]} without even mentioning the world's first working deep learning networks by Ivakhnenko and Lapa (1965).^{[DEEP12][R8]}
Moreover, LBH fail to cite Amari's 196768 work—which included computer simulations—on learning internal representations of multilayer perceptrons through stochastic gradient descent,^{[GD13]} almost two decades before LBH's first experimental work on learning internal representations.
★2. LBH^{[DL3a]} cite Hinton^{[GPUCNN4]} (2012) for "dropout" without mentioning that dropout is just a variant of Hanson's 1990 stochastic delta rule which he did not cite.^{[Drop14][GPUCNN4]}
★3. Several times, LBH^{[DL3a]} mention backpropagation—and LBH's papers on applications of this algorithm—but neither its inventor Linnainmaa (1970),^{[BP15][BPAC]} nor its first application to NNs by Werbos (1982),^{[BP2]} nor Kelley's precursor of the method (1960).^{[BPA][T22]}
Some claim that the backpropagation algorithm is just the chain rule of Leibniz (1676)^{[LEI0710][DLH]} popularised by L'Hopital (1696).^{[CONN21]} No, it is the efficient way of applying the chain rule to big networks with differentiable nodes (there are also many inefficient ways of doing this).^{[T22]} It was not published until 1970.^{[BP1,4,5]}
★4. LBH^{[DL3a]} devote an extra section to rectified linear units (ReLUs), citing papers of the 2000s by Hinton and his former students, without citing Fukushima who introduced ReLUs in 1969.^{[RELU12]}
★5. LBH^{[DL3a]} refer to LeCun's work on CNNs, citing neither Fukushima—who created the basic CNN architecture in the 1970s,^{[CNN14]} nor Waibel—who in 1987 was the first to combine NNs with convolutions with backpropagation^{[BP16]} and weight sharing—nor the first backproptrained twodimensional CNNs of Zhang (1988).^{[CNN1a+]} Modern CNNs originated before LeCun's team helped to improve them.
★6. LBH^{[DL3a]} cite Hinton (1981) for multiplicative gating, without mentioning Ivakhnenko and Lapa who had multiplicative gating in deep networks already in 1965.^{[DEEP12][R8]}
★7. LBH^{[DL3a]} cite the "fast weights" of Hinton (1987) without mentioning the earlier fast weights of v. d. Malsburg (1981) and Feldman (1982).^{[FAST,FASTab][FWP]}
★8. ACM lauds LeCun for "deep learning architectures that can manipulate structured data, such as graphs."^{[T19,T22]} However, such architectures were proposed by Sperduti, Goller, and Küchler in the 1990s^{[SP9397][GOL][KU]} before LeCun, who cited neither them nor our graph NNlike, Transformerlike Fast Weight Programmers of 1991^{[FWP01][FWP6][FWP]} which learn to continually rewrite NNbased mappings from queries to answers. See also Pollack's even earlier relevant work^{[PO8790]} and compare the important work of Baldi and colleagues.^{[BA9603]}
★9. Hinton's 1985 Boltzmann Machine paper about learning internal representations^{[BM]} lauded by ACM^{[T19]} neither cited relevant prior work by Sherrington & Kirkpatrick^{[SK75]} & Glauber^{[G63]} nor the first working algorithms for deep learning of internal representations (Ivakhnenko & Lapa, 1965)^{[DEEP12][HIN]} nor Amari's work (196768)^{[GD12]} on learning internal representations in deep nets endtoend through stochastic gradient descent. Even later surveys by the authors^{[S20][DLC]} failed to cite the prior art.^{[T22]}
Here one must also mention the unfortunate role of the wellknown N(eur)IPS conference. Hinton's 1985 coauthor Sejnowski^{[BM]} has been its president for decades. Over the years, N(eur)IPS has frequently invited LBH to give keynotes, especially Hinton, continually providing a platform for Sejnowski's and LBH's revisionist history of deep learning.^{[S20]} In our 2021 debate on the Connectionists Mailing List,^{[CONN21]} perhaps the oldest mailing list on NNs, I blasted Sejnowski's 2020 deep learning survey in PNAS^{[S20]} which wrongly claims that his 1985 Boltzmann machine^{[BM]} was the first NN to learn internal representations (see ★9 above), although it is wellknown that such networks emerged much earlier in Ukraine^{[DEEP12][HIN]} (1965) and Japan^{[GD12]} (1967). In an interview, Sejnowski claimed: "Our goal was to try to take a network with multiple layers—an input layer, an output layer and layers in between—and make it learn. It was generally thought, because of early work that was done in AI in the 60s, that no one would ever find such a learning algorithm because it was just too mathematically difficult." My reply was:^{[CONN21]} "You are a wellknown scientist, head of NeurIPS, and chief editor of a major journal. You must correct this. We must all be better than this as scientists. We owe it to both the past, present, and future scientists as well as those we ultimately serve." Generally speaking, I am encouraging N(eur)IPS to fight systemic academic corruption.
In summation, for decades, much of the more prominent work of Dr. Hinton, Dr. Bengio, Dr. LeCun has simply been repackaged versions of earlier work that they produced without proper citation.^{[T22]} The repetitive nature of LBH's and ACM's failures to uphold basic scientific standards represents a serious attack on the integrity of the field of Artificial Intelligence. If we, in turn, choose to ignore this, then we will be committing a sin against ourselves and our scientific predecessors.^{[T22]}
Apparently to avoid a factfocused scientific debate, Hinton and LeCun even conducted ad hominem attacks^{[AH23]} against me true to the motto: "If you cannot dispute a factbased message, attack the messenger himself."^{[HIN][T22][LEC]}
In particular, LeCun stated in the NY Times that "Jürgen ... keeps claiming credit he doesn't deserve for many, many things,"^{[NYT1]} without any evidence, without providing a single example.^{[T22]} Likewise, in the popular science venue ZDNet,^{[LEC22c]} he made wrong and misleading claims about my work, without any justification, without any references. I debunked these claims in Addendum III of [LEC]. In conjunction with previous work,^{[T22][LEC]} the present piece makes clear that it is actually LBH themselves who "keep claiming credit they doesn't deserve for many, many things," providing numerous examples, plus the references to back them up.
Fortunately, unlike politics, however, science is immune to ad hominem attacks—at least in the long run. In the hard sciences, the only things that count are the facts. Science is not democratic. If 100 persons claim one thing, and only one person claims the opposite, but he/she can back it up through facts, then he/she wins. If you haven't already read it, see "100 Authors against Einstein."^{[AH1]}
ACM's Turing award for LBH may already have encouraged other machine learning researchers to follow in their footsteps and conduct what can only be described as bad science.^{}[T22][DLH] Some seem to think: if these guys can get away with it, I can do so, too. In particular, certain researchers at companies that employed Hinton & LeCun apparently felt encouraged to abandon scientific integrity as well (while those companies remained deeply influenced by our contributions^{[DL4][DEC]}).
The famous ResNet paper^{[HW2]} was published by Microsoft, and its first author was hired by Meta (formerly Facebook). The paper did cite our earlier Highway Net,^{[HW13]} but did not explicitly mention that ResNet is an (opengated) version of the Highway Net (a ResNet is like a Highway Net whose gates are set to 1.0).
Google published a famous 2017 paper on attentionbased "quadratic" Transformers^{[TR1][FWP]} without mentioning my closely related unnormalised "linear" Transformers of 1991,^{[FWP,FWP01,6]} not even in later papers after the formal connection was very concretely pointed out to them in a peerreviewed publication (2021).^{[FWP6]} The 1991 Transformer variant^{[FWP01]} already learned to generate what's now called KEY and VALUE patterns^{[TR1]} to create an efficient linearized version of what's now called "selfattention,"^{[TR1]} in 1993 called "internal spotlights of attention."^{[FWP2]} Sec. 2 of the blog post^{[FWP]} reviews the roles of QUERY/KEY/VALUE patterns in linear (1991) and quadratic (2017) Transformers. Google has done great work scaling this old principle up but should be noting this connection now that it's known. See also disputes B4, H4.
Google also acquired the company DeepMind (cofounded by a student from my lab^{[MIR]}) which published quite a few wellknown papers that did not mention our closely related earlier work.^{[DM1][DNC][NAN5]}
The first author of a 2014 paper on GANs^{[GAN1]} (an instance of my ancient Artificial Curiosity^{[AC9020]}) went to Apple and then to Google DeepMind, where he never admitted that his paper contains wrong statements about our earlier work—see dispute B1. Even those with a terminally short attention span can easily find many additional examples of recent misattributions. The company Meta (whose Chief AI Scientist is LeCun) released LLaMA 2 in 2023. As a large language model, LLaMA inherits from many of my 1991 ideas [FWP,FWP02,6][UN01][MOST].
In 2023, the company Meta (whose Chief AI Scientist is LeCun) released its LLaMA 2 software. As a pretrained Transformerbased large language model, LLaMA inherits many of my 1991 ideas.^{[FWP,FWP02,6][UN01][MOST]} However, LLaMA has propagated provably false rhetoric, to the detriment of science itself, claiming that I "have been involved in harmful activities" and have not made "positive contributions to society, such as pioneers in their field." See this tweet (25 July 2023) and disputes B4, H4, H1. Obviously, large language models can be used to propagate a misleading history of AI.
For example, LeCun & Bengio & Hinton (LBH) have been called "godfathers of AI,"^{[GF2]} based on a suggestion of one of Hinton's former students. This makes sense only from the "deep learning mafia"^{[DLC2]} point of view: for decades, LBH have kept renaming inventions of other researchers, without citing the original inventors. That is, these socalled godfathers baptised creations fathered by others without approval of the fathers.^{[T22]} The sections above are full of examples.
Who were the true AI pioneers? The 20th century's "father of practical AI" was Leonardo Torres y Quevedo.^{[DLH]} He built the first working chess end game player in 1914^{[BRU14]} (back then chess was considered as an activity restricted to the realms of intelligent creatures).
Quevedo's machine was still considered impressive decades later when another AI pioneer, Norbert Wiener,^{[WI48]}—played against it at the 1951 Paris conference, now often viewed as the first conference on AI,^{[AI51][BRO21][BRU4]}—predating the later 1956 Dartmouth conference where the name "AI" was coined by John McCarthy and colleagues, the true "godfathers of AI," who introduced a new name for works of earlier "fathers of AI."
The "father of AI theory" was Kurt Gödel, who, in 193134, identified fundamental limits of any type of computationbased AI—and of computation/theorem proving/math in general.^{[GOD][BIB3][GOD21,a]} In 1935, Alonzo Church^{[CHU]}—and then in 193637 also Alan Turing^{[TUR][TUR21]}—extended Gödel's result; later Church and Turing also discussed and named certain aspects of AI.^{[TUR13ab][TUR21]}
In modern AI, deep learning is king. The "fathers of deep learning" were Alexey Ivakhnenko and Valentin Lapa, who, in 1965, had the first general, working learning algorithm for deep neural networks with many hidden layers.^{[DEEP12][DL12][DLH]}
With a similar logic to McCarthy et al., Aizenberg et al. might be called the "godfathers of deep learning," because they introduced the ancient term "deep learning" to NNs in 2000 (after Rina Dechter introduced it to AI/ML in 1986).^{[DL2]}
The mathematical roots of deep learning, however, go back centuries: the chain rule—the heart of modern deep learning—is due to Gottfried Wilhelm Leibniz (1676), the first NNs (now called linear NNs) are due to Johann Carl Friedrich Gauss & AdrienMarie Legendre (circa 1800).^{[DLH]} The recent survey^{[DLH]} lists many additional pioneers of the field who may merit yetunclaimed titles not mentioned above.
Like those who know me can testify, finding and citing original sources of scientific and technological innovations is important to me, whether they are mine or other people's.^{}[DL12][DLH][HIN][T22][NASC19] The present page is offered as a resource for all good scientists who share this inclination.
LBH and their coworkers have contributed certain useful improvements of existing deep learning methods.^{[CNN2,4][CDI][LAN][RMSP][XAV][ATT14][CAPS]} The essential foundations of deep learning, however, were laid by others whom they did not cite.^{[T22]}
Remarkably, four of the numerous priority disputes mentioned above (H1, H2, B7, L2) are related to my 1991 paper^{[UN01][UN]} which in many ways kickstarted what people now call deep learning, going beyond Ivakhnenko's "early" deep learning^{[DEEP12]} which LBH did not cite either.^{[DLC]} In fact, most of the disputes go back to the work in our Annus Mirabilis 199091:^{[MIR]} B1, B2, B4, B7, H1, H2, H3, H4, L1, L2.
Since one cannot easily look into the minds of people, we cannot exclude the possibility, however small, that all misattributions mentioned above were unintentional^{[PLAG16][CONN21]} rather than intentional.^{[FAKE2]} This might even be forgivable to some extent save for the fact that LBH have not corrected their misattributions and selfaggrandizations in later surveys.^{[DL33a][T22]} From the standpoint of scientific integrity, this is unacceptable.
The deontology of science enforces proper scientific standards and behavior when it comes to identifying prior art and assigning credit. Science has a wellestablished way of dealing with "multiple discovery" and plagiarism, be it unintentional^{[PLAG16][CONN21]} or not,^{[FAKE2]} based on facts such as time stamps of publications and patents. Sometimes it may take a while to settle disputes, but in the end, the facts must always win. As long as the facts have not yet won, it's not yet the end. No fancy award can ever change that.^{[HIN][T22]}
As Elvis Presley put it, "Truth is like the sun. You can shut it out for a time, but it ain't goin' away."^{[T22]}
On social media, a frequent but misinformed comment on all this is that it sometimes took me years to notice connections of recent papers to our old work of the 1990s. However, there are hundreds of new AI papers each month, and it is impossible to catch immediately all cases of intentional or accidental plagiarism. A new paper may take years to become visible enough to attract my attention, and only then I'll recognize what's a rehash of previous work. Anyway, all of this is irrelevant in science: it's not the job of old authors (who might be dead already) to study new related work; it's the job of new authors to study old related work!
Perhaps the greatest existential risk to scientific integrity is that the ongoing attack on it isn't discussed more yet.^{[T22]} Dear reader, let me urge you: don't become part of the problem! For example, don't simply cite (without rectification) a flawed paper that misrepresents or does not mention earlier relevant work, just because it was cited by many others. Don't be prey to the ancient idiom: "eat dung—a billion flies can't be wrong!"^{[T22]} Have you failed to correctly assign credit in the past? Then you must rectify this in future publications. Don't participate in systemic academic corruption. Don't wait until your name appears on the pillory list of Sec. 8. It's a shame for our field that such elementary rules of scientific conduct need to be emphasized.^{[T22]}
To discourage AI plagiarism in the future, I propose to establish a web site listing papers on deep learning and AI that duplicate previous work without proper attribution, and without rectifying this in later publications: the pillory list you don't want to be on. An international unbiased committee of AI experts should be established (perhaps with the help of ACM^{}[ACM1823]) to discuss candidate papers brought to their attention by members of the machine learning community, and decide about adding papers to the list, or removing them once the concerns have been adequately addressed. The following paragraphs list a few older nonLBH candidate cases and may be viewed as a start.
Case 1. Around 1960, Frank Rosenblatt not only had linear NNs plus threshold functions, he also had much more interesting MLPs with a nonlearning first layer with randomized weights and an adaptive output layer.^{[R62]} So Rosenblatt basically had what much later was rebranded by Huang and others as Extreme Learning Machines (ELMs)^{[ELM1]}
without proper attribution.^{[DLH]} The
revisionist narrative of ELMs^{[ELM2][CONN21]}
is a bit like the revisionist narrative of deep learning criticized by the present report. The "ELM conspiracy" apparently feels they can get away with outrageous improper credit assignment, just like the selfproclaimed
Case 2. In 1972, ShunIchi Amari made the original LenzIsing recurrent architecture^{[L20][I24,I25][K41][W45][T22]} adaptive such that it could learn to associate input patterns with output patterns by changing its connection weights.^{[AMH1]} 10 years later, the Amari network was republished by Hopfield^{[AMH2]} who did not cite Amari, not even in later papers. Subsequently, this network was frequently called the Hopfield Network!^{[DLH]} See also this tweet of 2022.
Case 3. ... (to be continued)
...
Some of the material above was taken from previous AI Blog posts.^{[MIR][DEC][GOD21] [LEI21][AC][ATT][DAN] [DAN1][DL4][GPUCNN5,8][DLC][DLH][FWP][LEC] [META][MLP2][MOST] [PLAN][UN][LSTMPG][BP4] [DL6a][HIN][T22]} Thanks to many expert reviewers (including several famous neural net pioneers) for useful comments.^{[T22]} Since science is about selfcorrection, let me know under juergen@idsia.ch if you can spot any remaining error. Many additional relevant publications can be found in my publication page and my arXiv page. This work is licensed under a Creative Commons AttributionNonCommercialShareAlike 4.0 International License.
[25y97] In 2022, we are celebrating the following works from a quartercentury ago. 1. Journal paper on Long ShortTerm Memory, the most cited neural network (NN) of the 20th century (and basis of the most cited NN of the 21st). 2. First paper on physical, philosophical and theological consequences of the simplest and fastest way of computing all possible metaverses (= computable universes). 3. Implementing artificial curiosity and creativity through generative adversarial agents that learn to design abstract, interesting computational experiments. 4. Journal paper on metareinforcement learning. 5. Journal paper on hierarchical Qlearning. 6. First paper on reinforcement learning to play soccer: start of a series. 7. Journal papers on flat minima & lowcomplexity NNs that generalize well. 8. Journal paper on LowComplexity Art, the Minimal Art of the Information Age. 9. Journal paper on probabilistic incremental program evolution.
[AC] J. Schmidhuber (AI Blog, 2021). 3 decades of artificial curiosity & creativity. Schmidhuber's artificial scientists not only answer given questions but also invent new questions. They achieve curiosity through: (1990) the principle of generative adversarial networks, (1991) neural nets that maximise learning progress, (1995) neural nets that maximise information gain (optimally since 2011), (1997) adversarial design of surprising computational experiments, (2006) maximizing compression progress like scientists/artists/comedians do, (2011) PowerPlay... Since 2012: applications to real robots.
[AC90] J. Schmidhuber. Making the world differentiable: On using selfsupervised fully recurrent neural networks for dynamic reinforcement learning and planning in nonstationary environments. Technical Report FKI12690, TUM, Feb 1990, revised Nov 1990. PDF. The first paper on planning with reinforcement learning recurrent neural networks (NNs) (more) and on generative adversarial networks where a generator NN is fighting a predictor NN in a minimax game (more).
[AC90b] J. Schmidhuber. A possibility for implementing curiosity and boredom in modelbuilding neural controllers. In J. A. Meyer and S. W. Wilson, editors, Proc. of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pages 222227. MIT Press/Bradford Books, 1991. PDF. More.
[AC91] J. Schmidhuber. Adaptive confidence and adaptive curiosity. Technical Report FKI14991, Inst. f. Informatik, Tech. Univ. Munich, April 1991. PDF.
[AC91b] J. Schmidhuber. Curious modelbuilding control systems. Proc. International Joint Conference on Neural Networks, Singapore, volume 2, pages 14581463. IEEE, 1991. PDF.
[AC97] J. Schmidhuber. What's interesting? Technical Report IDSIA3597, IDSIA, July 1997. Focus on automatic creation of predictable internal abstractions of complex spatiotemporal events: two competing, intrinsically motivated agents agree on essentially arbitrary algorithmic experiments and bet on their possibly surprising (not yet predictable) outcomes in zerosum games, each agent potentially profiting from outwitting / surprising the other by inventing experimental protocols where both modules disagree on the predicted outcome. The focus is on exploring the space of general algorithms (as opposed to traditional simple mappings from inputs to outputs); the general system focuses on the interesting things by losing interest in both predictable and unpredictable aspects of the world. Unlike Schmidhuber et al.'s previous systems with intrinsic motivation,^{[AC90AC95]} the system also takes into account the computational cost of learning new skills, learning when to learn and what to learn. See later publications.^{[AC99][AC02]}
[AC99] J. Schmidhuber. Artificial Curiosity Based on Discovering Novel Algorithmic Predictability Through Coevolution. In P. Angeline, Z. Michalewicz, M. Schoenauer, X. Yao, Z. Zalzala, eds., Congress on Evolutionary Computation, p. 16121618, IEEE Press, Piscataway, NJ, 1999.
[AC02] J. Schmidhuber. Exploring the Predictable. In Ghosh, S. Tsutsui, eds., Advances in Evolutionary Computing, p. 579612, Springer, 2002. PDF.
[AC06] J. Schmidhuber. Developmental Robotics, Optimal Artificial Curiosity, Creativity, Music, and the Fine Arts. Connection Science, 18(2): 173187, 2006. PDF.
[AC09] J. Schmidhuber. Art & science as byproducts of the search for novel patterns, or data compressible in unknown yet learnable ways. In M. Botta (ed.), Et al. Edizioni, 2009, pp. 98112. PDF. (More on artificial scientists and artists.)
[AC10] J. Schmidhuber. Formal Theory of Creativity, Fun, and Intrinsic Motivation (19902010). IEEE Transactions on Autonomous Mental Development, 2(3):230247, 2010. IEEE link. PDF. With a brief summary of the generative adversarial neural networks of 1990^{[AC90,90b][AC20]} where a generator NN is fighting a predictor NN in a minimax game (more).
[AC20] J. Schmidhuber. Generative Adversarial Networks are Special Cases of Artificial Curiosity (1990) and also Closely Related to Predictability Minimization (1991). Neural Networks, Volume 127, p 5866, 2020. Preprint arXiv/1906.04493.
[ACM18] ACM Code of Ethics and Professional Conduct. Association for Computing Machinery (ACM), 2018. Quote: "Computing professionals should therefore credit the creators of ideas, inventions, work, and artifacts, and respect copyrights, patents, trade secrets, license agreements, and other methods of protecting authors' works."
[ACM23] Policy for Honors Conferred by ACM. Association for Computing Machinery (ACM), 2023. Quote: "ACM also retains the right to revoke an Honor previously granted if ACM determines that it is in the best interests of the field to do so." Copy in the Internet Archive (2023).
[AH1] Hentschel K. (1996) A. v. Brunn: Review of "100 Authors against Einstein" [March 13, 1931]. In: Hentschel K. (eds) Physics and National Socialism. Science Networks—Historical Studies, vol 18. Birkhaeuser Basel. Link.
[AH2] F. H. van Eemeren, B. Garssen & B. Meuffels. The disguised abusive ad hominem empirically investigated: Strategic manoeuvring with direct personal attacks. Journal Thinking & Reasoning, Vol. 18, 2012, Issue 3, p. 344364. Link.
[AH3] D. Walton (PhD Univ. Toronto, 1972), 1998. Ad hominem arguments. University of Alabama Press.
[AIB] J. Schmidhuber. AI Blog. Includes variants of chapters of the AI Book.
[AI51] Les Machines a Calculer et la Pensee Humaine: Paris, 8.13. Januar 1951, Colloques internationaux du Centre National de la Recherche Scientifique; no. 37, Paris 1953. [H. Bruderer rightly calls that the first conference on AI.]
[AM16] Blog of Werner Vogels, CTO of Amazon (Nov 2016): Amazon's Alexa "takes advantage of bidirectional long shortterm memory (LSTM) networks using a massive amount of data to train models that convert letters to sounds and predict the intonation contour. This technology enables high naturalness, consistent intonation, and accurate processing of texts."
[AMH0] S. I. Amari (1972). Characteristics of random nets of analog neuronlike elements. IEEE Trans. Syst. Man Cybernetics, 2, 643657. First published 1969 in Japanese, long before Wilson & Cowan's very similar work (197273).
[AMH1] S. I. Amari (1972). Learning patterns and pattern sequences by selforganizing nets of threshold elements. IEEE Transactions, C 21, 11971206, 1972. PDF. First publication of what was later sometimes called the Hopfield network^{[AMH2]} or AmariHopfield Network,^{[AMH3]} based on the (uncited) LenzIsing recurrent architecture.^{[L20][I25][T22]} See also this tweet.
[AMH1b] W. A. Little. The existence of persistent states in the brain. Mathematical Biosciences, 19.12, p. 101120, 1974. Mentions the recurrent Ising model^{[L20][I25]}on which the (uncited) Amari network^{[AMH1,2]} is based.
[AMH2] J. J. Hopfield (1982). Neural networks and physical systems with emergent collective computational abilities. Proc. of the National Academy of Sciences, vol. 79, pages 25542558, 1982. The Hopfield network or AmariHopfield Network was first published in 1972 by Amari.^{[AMH1]} [AMH2] did not cite [AMH1].
[AMH3] A. P. Millan, J. J. Torres, J. Marro. How Memory Conforms to Brain Development. Front. Comput. Neuroscience, 2019
[AOI] M. Ford. Architects of Intelligence: The truth about AI from the people building it. Packt Publishing, 2018. Preface to German edition by J. Schmidhuber.
[ATT] J. Schmidhuber (AI Blog, 2020). 30year anniversary of endtoend differentiable sequential neural attention. Plus goalconditional reinforcement learning. Schmidhuber had both hard attention (1990) and soft attention (199193).^{[FWP]} Today, both types are very popular.
[ATT0] J. Schmidhuber and R. Huber. Learning to generate focus trajectories for attentive vision. Technical Report FKI12890, Institut für Informatik, Technische Universität München, 1990. PDF.
[ATT1] J. Schmidhuber and R. Huber. Learning to generate artificial fovea trajectories for target detection. International Journal of Neural Systems, 2(1 & 2):135141, 1991. Based on TR FKI12890, TUM, 1990. PDF. More.
[ATT2] J. Schmidhuber. Learning algorithms for networks with internal and external feedback. In D. S. Touretzky, J. L. Elman, T. J. Sejnowski, and G. E. Hinton, editors, Proc. of the 1990 Connectionist Models Summer School, pages 5261. San Mateo, CA: Morgan Kaufmann, 1990. PS. (PDF.) Reviewed by Dr. Hinton.
[ATT3] H. Larochelle, G. E. Hinton. Learning to combine foveal glimpses with a thirdorder Boltzmann machine. NIPS 2010. This work is very similar to [ATT02] which the authors did not cite. In fact, Hinton was the reviewer of a 1990 paper^{[ATT2]} which summarised in its Section 5 Schmidhuber's early work on attention: the first implemented neural system for combining glimpses that jointly trains a recognition & prediction component with an attentional component (the fixation controller). Two decades later, Hinton wrote about his own work:^{[ATT3]} "To our knowledge, this is the first implemented system for combining glimpses that jointly trains a recognition component ... with an attentional component (the fixation controller)." See [MIR](Sec. 9)[R4].
[ATT14] D. Bahdanau, K. Cho, Y. Bengio. Neural Machine Translation by Jointly Learning to Align and Translate. 201416. Preprint arXiv/1409.0473, 201416. This work on soft "attention" did not cite Schmidhuber's much earlier original work of 19911993 on soft attention and Transformers with linearized selfattention.^{[FWP,FWP02,6][ATT]}
[AV1] A. Vance. Google Amazon and Facebook Owe Jürgen Schmidhuber a Fortune—This Man Is the Godfather the AI Community Wants to Forget. Business Week, Bloomberg, May 15, 2018.
[AV2] A. Vance. Apple and Its Rivals Bet Their Futures on These Men's Dreams. Business Week, Bloomberg, May 17, 2018.
[BA93] P. Baldi and Y. Chauvin. Neural Networks for Fingerprint Recognition, Neural Computation, Vol. 5, 3, 402418, (1993). First application of CNNs with backpropagation to biomedical/biometric images.
[BA96] P. Baldi and Y. Chauvin. Hybrid Modeling, HMM/NN Architectures, and Protein Applications, Neural Computation, Vol. 8, 7, 15411565, (1996). One of the first papers on graph neural networks.
[BA99] P. Baldi, S. Brunak, P. Frasconi, G. Soda, and G. Pollastri. Exploiting the Past and the Future in Protein Secondary Structure Prediction, Bioinformatics, Vol. 15, 11, 937946, (1999).
[BA03] P. Baldi and G. Pollastri. The Principled Design of LargeScale Recursive Neural Network ArchitecturesDAGRNNs and the Protein Structure Prediction Problem. Journal of Machine Learning Research, 4, 575602, (2003).
[BB2] J. Schmidhuber. A local learning algorithm for dynamic feedforward and recurrent networks. Connection Science, 1(4):403412, 1989. (The Neural Bucket Brigade—figures omitted!). PDF. HTML. Compare TR FKI12490, TUM, 1990. PDF. Proposal of a biologically more plausible deep learning algorithm that—unlike backpropagation—is local in space and time. Based on a "neural economy" for reinforcement learning. See also this tweet.
[BIB3] W. Bibel (2003). Mosaiksteine einer Wissenschaft vom Geiste. Invited talk at the conference on AI and Gödel, Arnoldsheim, 46 April 2003. Manuscript, 2003.
[BM] D. Ackley, G. Hinton, T. Sejnowski (1985). A Learning Algorithm for Boltzmann Machines. Cognitive Science, 9(1):147169. This paper neither cited relevant prior work by Sherrington & Kirkpatrick^{[SK75]} & Glauber^{[G63]} nor the first working algorithms for deep learning of internal representations (Ivakhnenko & Lapa, 1965)^{[DEEP12][HIN]} nor Amari's work (196768)^{[GD12]} on learning internal representations in deep nets through stochastic gradient descent. Even later surveys by the authors^{[S20][DLC]} failed to cite the prior art.^{[T22]}
[BOU] H Bourlard, N Morgan (1993). Connectionist speech recognition. Kluwer, 1993.
[BPA] H. J. Kelley. Gradient Theory of Optimal Flight Paths. ARS Journal, Vol. 30, No. 10, pp. 947954, 1960. Precursor of modern backpropagation.^{[BP14]}
[BPB] A. E. Bryson. A gradient method for optimizing multistage allocation processes. Proc. Harvard Univ. Symposium on digital computers and their applications, 1961.
[BPC] S. E. Dreyfus. The numerical solution of variational problems. Journal of Mathematical Analysis and Applications, 5(1): 3045, 1962.
[BP1] S. Linnainmaa. The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors. Master's Thesis (in Finnish), Univ. Helsinki, 1970. See chapters 67 and FORTRAN code on pages 5860. PDF. See also BIT 16, 146160, 1976. Link. The first publication on "modern" backpropagation, also known as the reverse mode of automatic differentiation.
[BP2] P. J. Werbos. Applications of advances in nonlinear sensitivity analysis. In R. Drenick, F. Kozin, (eds): System Modeling and Optimization: Proc. IFIP, Springer, 1982. PDF. First application of backpropagation^{[BP1]} to NNs (concretizing thoughts in Werbos' 1974 thesis).
[BP4] J. Schmidhuber (AI Blog, 2014; updated 2020). Who invented backpropagation? More.^{[DL2]}
[BP5] A. Griewank (2012). Who invented the reverse mode of differentiation? Documenta Mathematica, Extra Volume ISMP (2012): 389400.
[BPTT1] P. J. Werbos. Backpropagation through time: what it does and how to do it. Proceedings of the IEEE 78.10, 15501560, 1990.
[BPTT2] R. J. Williams and D. Zipser. Gradientbased learning algorithms for recurrent networks. In: Backpropagation: Theory, architectures, and applications, p 433, 1995.
[BRI] Bridle, J.S. (1990). AlphaNets: A Recurrent "Neural" Network Architecture with a Hidden Markov Model Interpretation, Speech Communication, vol. 9, no. 1, pp. 8392.
[BRU1] H. Bruderer. Computing history beyond the UK and US: selected landmarks from continental Europe. Communications of the ACM 60.2 (2017): 7684.
[BRU2] H. Bruderer. Meilensteine der Rechentechnik. 2 volumes, 3rd edition. Walter de Gruyter GmbH & Co KG, 2020.
[BRU3] H. Bruderer. Milestones in Analog and Digital Computing. 2 volumes, 3rd edition. Springer Nature Switzerland AG, 2020.
[BRU4] H. Bruderer. The Birthplace of Artificial Intelligence? Communications of the ACM, BLOG@CACM, Nov 2017. Link.
[BRO21] D. C. Brock (2021). Cybernetics, Computer Design, and a Meeting of the Minds. An influential 1951 conference in Paris considered the computer as a model of—and for—the human mind. IEEE Spectrum, 2021. Link.
[BW] H. Bourlard, C. J. Wellekens (1989). Links between Markov models and multilayer perceptrons. NIPS 1989, p. 502510.
[CAPS] S. Sabour, N. Frosst, G. E. Hinton (2017). Dynamic routing between capsules. Proc. NIPS 2017, pp. 38563866.
[CDI] G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural computation 14.8 (2002): 17711800.
[CHU] A. Church (1935). An unsolvable problem of elementary number theory. Bulletin of the American Mathematical Society, 41: 332333. Abstract of a talk given on 19 April 1935, to the American Mathematical Society. Also in American Journal of Mathematics, 58(2), 345363 (1 Apr 1936). First explicit proof that the Entscheidungsproblem (decision problem) does not have a general solution.
[CNN1] K. Fukushima: Neural network model for a mechanism of pattern recognition unaffected by shift in position—Neocognitron. Trans. IECE, vol. J62A, no. 10, pp. 658665, 1979. The first deep convolutional neural network architecture, with alternating convolutional layers and downsampling layers. In Japanese. English version: [CNN1+]. More in Scholarpedia.
[CNN1+] K. Fukushima: Neocognitron: a selforganizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, vol. 36, no. 4, pp. 193202 (April 1980). Link.
[CNN1a] A. Waibel. Phoneme Recognition Using TimeDelay Neural Networks. Meeting of IEICE, Tokyo, Japan, 1987. First application of backpropagation^{[BP1][BP2]} and weightsharing to a convolutional architecture.
[CNN1a+] W. Zhang. Shiftinvariant pattern recognition neural network and its optical architecture. Proc. Annual Conference of the Japan Society of Applied Physics, 1988. First backpropagationtrained 2D CNN.
[CNN1b] A. Waibel, T. Hanazawa, G. Hinton, K. Shikano and K. J. Lang. Phoneme recognition using timedelay neural networks. IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 37, no. 3, pp. 328339, March 1989. Based on [CNN1a].
[CNN1c] Bower Award Ceremony 2021: Jürgen Schmidhuber lauds Kunihiko Fukushima. YouTube video, 2021.
[CNN2] Y. LeCun, B. Boser, J. S. Denker, D. Henderson, R. E. Howard, W. Hubbard, L. D. Jackel: Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation, 1(4):541551, 1989. PDF.
[CNN3a] K. Yamaguchi, K. Sakamoto, A. Kenji, T. Akabane, Y. Fujimoto. A Neural Network for SpeakerIndependent Isolated Word Recognition. First International Conference on Spoken Language Processing (ICSLP 90), Kobe, Japan, Nov 1990. An NN with convolutions using MaxPooling instead of Fukushima's Spatial Averaging.^{[CNN1]}
[CNN3] Weng, J., Ahuja, N., and Huang, T. S. (1993). Learning recognition and segmentation of 3D objects from 2D images. Proc. 4th Intl. Conf. Computer Vision, Berlin, Germany, pp. 121128. A CNN whose downsampling layers use MaxPooling (which has become very popular) instead of Fukushima's Spatial Averaging.^{[CNN1]}
[CNN4] M. A. Ranzato, Y. LeCun: A Sparse and Locally Shift Invariant Feature Extractor Applied to Document Images. Proc. ICDAR, 2007
[CNN5a] S. Behnke. Learning iterative image reconstruction in the neural abstraction pyramid. International Journal of Computational Intelligence and Applications, 1(4):427438, 1999.
[CNN5b] S. Behnke. Hierarchical Neural Networks for Image Interpretation, volume LNCS 2766 of Lecture Notes in Computer Science. Springer, 2003.
[CNN5c] D. Scherer, A. Mueller, S. Behnke. Evaluation of pooling operations in convolutional architectures for object recognition. In Proc. International Conference on Artificial Neural Networks (ICANN), pages 92101, 2010.
[CO1] J. Koutnik, F. Gomez, J. Schmidhuber (2010). Evolving Neural Networks in Compressed Weight Space. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO2010), Portland, 2010. PDF.
[CO2] J. Koutnik, G. Cuccu, J. Schmidhuber, F. Gomez. Evolving LargeScale Neural Networks for VisionBased Reinforcement Learning. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Amsterdam, July 2013. PDF. The first deep learning model to successfully learn control policies directly from highdimensional sensory input using reinforcement learning, without any unsupervised pretraining.
[CO3] R. K. Srivastava, J. Schmidhuber, F. Gomez. Generalized Compressed Network Search. Proc. GECCO 2012. PDF.
[CONN21] Since November 2021: Comments on earlier versions of the report^{[T22]} in the Connectionists Mailing List, perhaps the oldest mailing list on artificial neural networks. Link to the archive.
[CTC] A. Graves, S. Fernandez, F. Gomez, J. Schmidhuber. Connectionist Temporal Classification: Labelling Unsegmented Sequence Data with Recurrent Neural Networks. ICML 06, Pittsburgh, 2006. PDF.
[CUB0] R. J. Williams. Complexity of exact gradient computation algorithms for recurrent neural networks. Technical Report NUCCS8927, Northeastern University, College of Computer Science, 1989.
[CW] J. Koutnik, K. Greff, F. Gomez, J. Schmidhuber. A Clockwork RNN. Proc. 31st International Conference on Machine Learning (ICML), p. 18451853, Beijing, 2014. Preprint arXiv:1402.3511 [cs.NE].
[DAN] J. Schmidhuber (AI Blog, 2021). 10year anniversary. In 2011, DanNet triggered the deep convolutional neural network (CNN) revolution. Named after my outstanding postdoc Dan Ciresan, it was the first deep and fast CNN to win international computer vision contests, and had a temporary monopoly on winning them, driven by a very fast implementation based on graphics processing units (GPUs). 1st superhuman result in 2011.^{[DAN1]} Now everybody is using this approach.
[DAN1] J. Schmidhuber (AI Blog, 2011; updated 2021 for 10th birthday of DanNet): First superhuman visual pattern recognition. At the IJCNN 2011 computer vision competition in Silicon Valley, our artificial neural network called DanNet performed twice better than humans, three times better than the closest artificial competitor (by LeCun's team), and six times better than the best nonneural method.
[DEC] J. Schmidhuber (AI Blog, 02/20/2020, revised 2022). The 2010s: Our Decade of Deep Learning / Outlook on the 2020s. The recent decade's most important developments and industrial applications based on our AI, with an outlook on the 2020s, also addressing privacy and data markets.
[DEEP1] Ivakhnenko, A. G. and Lapa, V. G. (1965). Cybernetic Predicting Devices. CCM Information Corporation. First working Deep Learners with many layers, learning internal representations.
[DEEP1a] Ivakhnenko, Alexey Grigorevich. The group method of data of handling; a rival of the method of stochastic approximation. Soviet Automatic Control 13 (1968): 4355.
[DEEP2] Ivakhnenko, A. G. (1971). Polynomial theory of complex systems. IEEE Transactions on Systems, Man and Cybernetics, (4):364378.
[DIST2] O. Vinyals, J. A. Dean, G. E. Hinton. Distilling the Knowledge in a Neural Network. Preprint arXiv:1503.02531 [stat.ML], 2015. The authors did not cite Schmidhuber's original 1991 NN distillation procedure,^{[UN02][MIR](Sec. 2)} not even in the later patent application US20150356461A1. See also this tweet.
[DL1] J. Schmidhuber, 2015. Deep learning in neural networks: An overview. Neural Networks, 61, 85117. More. Got the first Best Paper Award ever issued by the journal Neural Networks, founded in 1988.
[DL2] J. Schmidhuber, 2015. Deep Learning. Scholarpedia, 10(11):32832.
[DL3] Y. LeCun, Y. Bengio, G. Hinton (2015). Deep Learning. Nature 521, 436444. HTML. A "survey" of deep learning that does not mention the pioneering works of deep learning [T22].
[DL3a] Y. Bengio, Y. LeCun, G. Hinton (2021). Turing Lecture: Deep Learning for AI. Communications of the ACM, July 2021. HTML. Local copy (HTML only). Another "survey" of deep learning that does not mention the pioneering works of deep learning [T22].
[DL4] J. Schmidhuber (AI Blog, 2017). Our impact on the world's most valuable public companies: Apple, Google, Microsoft, Facebook, Amazon... By 201517, neural nets developed in my labs were on over 3 billion devices such as smartphones, and used many billions of times per day, consuming a significant fraction of the world's compute. Examples: greatly improved (CTCbased) speech recognition on all Android phones, greatly improved machine translation through Google Translate and Facebook (over 4 billion LSTMbased translations per day), Apple's Siri and Quicktype on all iPhones, the answers of Amazon's Alexa, etc. Google's 2019 ondevice speech recognition (on the phone, not the server) is still based on LSTM.
[DL6] F. Gomez and J. Schmidhuber. Coevolving recurrent neurons learn deep memory POMDPs. In Proc. GECCO'05, Washington, D. C., pp. 17951802, ACM Press, New York, NY, USA, 2005. PDF.
[DL6a] J. Schmidhuber (AI Blog, Nov 2020). 15year anniversary: 1st paper with "learn deep" in the title (2005). Our deep reinforcement learning & neuroevolution solved problems of depth 1000 and more.^{[DL6]} Soon after its publication, everybody started talking about "deep learning." Causality or correlation?
[DL7] "Deep Learning ... moving beyond shallow machine learning since 2006!" Web site deeplearning.net of Y. Bengio's MILA (2015, retrieved May 2020; compare the version in the Internet Archive), referring to Hinton's^{[UN4]} and Bengio's^{[UN5]} unsupervised pretraining for deep NNs^{[UN4]} (2006) although this type of deep learning dates back to Schmidhuber's work of 1991.^{[UN12][UN]} Compare Sec. II & XVII & III.
[DLC] J. Schmidhuber (AI Blog, June 2015). Critique of Paper by selfproclaimed^{[DLC12]} "Deep Learning Conspiracy" (Nature 521 p 436). The inventor of an important method should get credit for inventing it. She may not always be the one who popularizes it. Then the popularizer should get credit for popularizing it (but not for inventing it).
[DLC1] Y. LeCun. IEEE Spectrum Interview by L. Gomes, Feb 2015. Quote: "A lot of us involved in the resurgence of Deep Learning in the mid2000s, including Geoff Hinton, Yoshua Bengio, and myself—the socalled 'Deep Learning conspiracy' ..."
[DLC2] M. Bergen, K. Wagner (2015). Welcome to the AI Conspiracy: The 'Canadian Mafia' Behind Tech's Latest Craze. Vox recode, 15 July 2015. Quote: "... referred to themselves as the 'deep learning conspiracy.' Others called them the 'Canadian Mafia.'"
[DLH] J. Schmidhuber (AI Blog, 2022). Annotated History of Modern AI and Deep Learning. Technical Report IDSIA2222, IDSIA, Lugano, Switzerland, 2022. Preprint arXiv:2212.11279. Tweet of 2022.
[DM1] V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, M. Riedmiller. Playing Atari with Deep Reinforcement Learning. Tech Report, 19 Dec. 2013, arxiv:1312.5602.
[DM2] V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, D. Hassabis. Humanlevel control through deep reinforcement learning. Nature, vol. 518, p 1529, 26 Feb. 2015. Link. DeepMind's first famous paper. Its abstract claims: "While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, lowdimensional state spaces." It also claims to bridge "the divide between highdimensional sensory inputs and actions." Similarly, the first sentence of the abstract of the earlier tech report version^{[DM1]} of [DM2] claims to "present the first deep learning model to successfully learn control policies directly from highdimensional sensory input using reinforcement learning." However, the first such system (requiring no unsupervised pretraining) was created earlier by Jan Koutnik et al. in Schmidhuber's lab.^{[CO2]} DeepMind was cofounded by Shane Legs, a PhD student from this lab; he and Daan Wierstra (another PhD student of Schmidhuber and DeepMind's 1st employee) were the first persons at DeepMind who had AI publications and PhDs in computer science. More.
[DM3] S. Stanford. DeepMind's AI, AlphaStar Showcases Significant Progress Towards AGI. Medium ML Memoirs, 2019. Alphastar has a "deep LSTM core."
[DM4] J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Zidek, A. Potapenko, A. Bridgland, C. Meyer, S. A. A. Kohl, A. J. Ballard, A. Cowie, B. RomeraParedes, S. Nikolov, R. Jain, J. Adler, T. Back, S. Petersen, D. Reiman, E. Clancy, M. Zielinski, M. Steinegger, M. Pacholska, T. Berghammer, S. Bodenstein, D. Silver, O. Vinyals, A. W. Senior, K. Kavukcuoglu, P. Kohli & D. Hassabis. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583589, 2021. DeepMind's breakthrough application of deep learning did not cite Hochreiter et al.'s first successful application [HO07] of deep learning to protein folding (2007).
[DNC] A. Graves, G. Wayne, M. Reynolds, T. Harley, I. Danihelka, A. GrabskaBarwinska, S. G. Colmenarejo, E. Grefenstette, T. Ramalho, J. Agapiou, A. P. Badia, K. M. Hermann, Y. Zwols, G. Ostrovski, A. Cain, H. King, C. Summerfield, P. Blunsom, K. Kavukcuoglu, D. Hassabis. Hybrid computing using a neural network with dynamic external memory. Nature, 538:7626, p 471, 2016. This work of DeepMind did not cite the original work of the early 1990s on neural networks learning to control dynamic external memories.^{[PDA12][FWP01]}
[Drop1] S. J. Hanson (1990). A Stochastic Version of the Delta Rule, PHYSICA D,42, 265272. What's now called "dropout" is a variation of the stochastic delta rule—compare preprint arXiv:1808.03578, 2018.
[Drop2] N. FrazierLogue, S. J. Hanson (2020). The Stochastic Delta Rule: Faster and More Accurate Deep Learning Through Adaptive Weight Noise. Neural Computation 32(5):10181032.
[Drop3] J. Hertz, A. Krogh, R. Palmer (1991). Introduction to the Theory of Neural Computation. Redwood City, California: AddisonWesley Pub. Co., pp. 4546.
[Drop4] N. FrazierLogue, S. J. Hanson (2018). Dropout is a special case of the stochastic delta rule: faster and more accurate deep learning. Preprint arXiv:1808.03578, 2018.
[ELM1] G.B. Huang, Q.Y. Zhu, and C.K. Siew. Extreme learning machine: A new learning scheme of feedforward neural networks. Proc. IEEE Int. Joint Conf. on Neural Networks, Vol. 2, 2004, pp. 985990. This paper does not mention that the "ELM" concept goes back to Rosenblatt's work around 1960.^{[R62][T22]}
[ELM2] ELMORIGIN, 2004. The Official Homepage on Origins of Extreme Learning Machines (ELM). "Extreme Learning Machine Duplicates Others' Papers from 19882007." Local copy. This overview does not mention that the "ELM" concept goes back to Rosenblatt's work around 1960.^{[R62][T22]}
[FAKE] H. Hopf, A. Krief, G. Mehta, S. A. Matlin. Fake science and the knowledge crisis: ignorance can be fatal. Royal Society Open Science, May 2019. Quote: "Scientists must be willing to speak out when they see false information being presented in social media, traditional print or broadcast press" and "must speak out against false information and fake science in circulation and forcefully contradict public figures who promote it."
[FAKE2] L. Stenflo. Intelligent plagiarists are the most dangerous. Nature, vol. 427, p. 777 (Feb 2004). Quote: "What is worse, in my opinion, ..., are cases where scientists rewrite previous findings in different words, purposely hiding the sources of their ideas, and then during subsequent years forcefully claim that they have discovered new phenomena.
[FAST] C. v.d. Malsburg. Tech Report 812, Abteilung f. Neurobiologie, MaxPlanck Institut f. Biophysik und Chemie, Goettingen, 1981. First paper on fast weights or dynamic links.
[FASTa] J. A. Feldman. Dynamic connections in neural networks. Biological Cybernetics, 46(1):2739, 1982. 2nd paper on fast weights.
[FASTb] G. E. Hinton, D. C. Plaut. Using fast weights to deblur old memories. Proc. 9th annual conference of the Cognitive Science Society (pp. 177186), 1987. 3rd paper on fast weights (two types of weights with different learning rates).
[FAT1] H. Jones. Juergen Schmidhuber, Renowned 'Father Of Modern AI,' Says His Life's Work Won't Lead To Dystopia. Forbes Magazine, 26 May 2023. Link.
[FAT2] E. Colton. 'Father of AI' says tech fears misplaced: 'You cannot stop it'. Fox News, 7 May 2023. Link.
[FAT3] J. Taylor. Rise of artificial intelligence is inevitable but should not be feared, 'father of AI' says. The Guardian, 7 May 2023. Link.
[FB17] By 2017, Facebook used LSTM to handle over 4 billion automatic translations per day (The Verge, August 4, 2017); see also Facebook blog by J.M. Pino, A. Sidorov, N.F. Ayan (August 3, 2017)
[FM] S. Hochreiter and J. Schmidhuber. Flat minimum search finds simple nets. Technical Report FKI20094, Fakultät für Informatik, Technische Universität München, December 1994. PDF.
[FWP] J. Schmidhuber (AI Blog, 26 March 2021, updated 2023). 26 March 1991: Neural nets learn to program neural nets with fast weights—like Transformer variants. 2021: New stuff! In 2022, ChatGPT took the world by storm, generating large volumes of text that are almost indistinguishable from what a human might write.^{[GPT3]} ChatGPT and similar large language models (LLMs) are based on a family of artificial neural networks (NNs) called Transformers.^{[TR12]} Already in 1991, when compute was a million times more expensive than today, Schmidhuber published the first Transformer variant, which is now called an unnormalised linear Transformer.^{[FWP01,6][TR56]} That wasn't the name it got given at the time, but today the mathematical equivalence is obvious. In a sense, computational restrictions drove it to be even more efficient than later "quadratic" Transformer variants,^{[TR12]} resulting in costs that scale linearly in input size, rather than quadratically. In the same year, Schmidhuber also introduced selfsupervised pretraining for deep NNs, now used to train LLMs (the "P" in "GPT" stands for "pretrained").^{[UN][UN03]} In 1993, he introduced the attention terminology^{[FWP2]} now used in this context,^{[ATT]} and extended the approach to recurrent NNs that program themselves. See tweet of 2022.
[FWP0] J. Schmidhuber. Learning to control fastweight memories: An alternative to recurrent nets. Technical Report FKI14791, Institut für Informatik, Technische Universität München, 26 March 1991. PDF. First paper on fast weight programmers that separate storage and control: a slow net learns by gradient descent to compute weight changes of a fast net. The outer productbased version (Eq. 5) is now known as an "unnormalised linear Transformer."^{[FWP]} That wasn't the name it got given at the time, but today the mathematical equivalence is obvious. In a sense, computational restrictions drove it to be even more efficient than later "quadratic" Transformer variants,^{[TR12]} resulting in costs that scale linearly in input size, rather than quadratically.
[FWP1] J. Schmidhuber. Learning to control fastweight memories: An alternative to recurrent nets. Neural Computation, 4(1):131139, 1992. Based on [FWP0]. PDF. See tweet of 2022 for 30year anniversary. Overview.
[FWP2] J. Schmidhuber. Reducing the ratio between learning complexity and number of timevarying variables in fully recurrent nets. In Proceedings of the International Conference on Artificial Neural Networks, Amsterdam, pages 460463. Springer, 1993. PDF. First recurrent NNbased fast weight programmer using outer products, introducing the terminology of learning "internal spotlights of attention."
[FWP3] I. Schlag, J. Schmidhuber. Gated Fast Weights for OnTheFly Neural Program Generation. Workshop on MetaLearning, @N(eur)IPS 2017, Long Beach, CA, USA.
[FWP3a] I. Schlag, J. Schmidhuber. Learning to Reason with Third Order Tensor Products. Advances in Neural Information Processing Systems (N(eur)IPS), Montreal, 2018. Preprint: arXiv:1811.12143. PDF.
[FWP4a] J. Ba, G. Hinton, V. Mnih, J. Z. Leibo, C. Ionescu. Using Fast Weights to Attend to the Recent Past. NIPS 2016. PDF. Very similar to [FWP02], in both motivation [FWP2] and execution.
[FWP4b] D. Bahdanau, K. Cho, Y. Bengio (2014). Neural Machine Translation by Jointly Learning to Align and Translate. Preprint arXiv:1409.0473 [cs.CL]. This work on "attention" did not cite Schmidhuber's much earlier original work of 19911993 on soft attention and Transformers with linearized selfattention.^{[FWP,FWP02,6][ATT]}
[FWP4d] Y. Tang, D. Nguyen, D. Ha (2020). Neuroevolution of SelfInterpretable Agents. Preprint: arXiv:2003.08165.
[FWP5] F. J. Gomez and J. Schmidhuber. Evolving modular fastweight networks for control. In W. Duch et al. (Eds.): Proc. ICANN'05, LNCS 3697, pp. 383389, SpringerVerlag Berlin Heidelberg, 2005. PDF. HTML overview. Reinforcementlearning fast weight programmer.
[FWP6] I. Schlag, K. Irie, J. Schmidhuber. Linear Transformers Are Secretly Fast Weight Programmers. ICML 2021. Preprint: arXiv:2102.11174.
[FWP7] K. Irie, I. Schlag, R. Csordas, J. Schmidhuber. Going Beyond Linear Transformers with Recurrent Fast Weight Programmers. NeurIPS 2021. Preprint: arXiv:2106.06295 (June 2021).
[FWPMETA1] J. Schmidhuber. Steps towards `selfreferential' learning. Technical Report CUCS62792, Dept. of Comp. Sci., University of Colorado at Boulder, November 1992. First recurrent fast weight programmer that can learn to run a learning algorithm or weight change algorithm on itself.
[FWPMETA2] J. Schmidhuber. A selfreferential weight matrix. In Proceedings of the International Conference on Artificial Neural Networks, Amsterdam, pages 446451. Springer, 1993. PDF. See also this tweet.
[FWPMETA3] J. Schmidhuber. An introspective network that can learn to run its own weight change algorithm. In Proc. of the Intl. Conf. on Artificial Neural Networks, Brighton, pages 191195. IEE, 1993.
[FWPMETA4] J. Schmidhuber. A neural network that embeds its own metalevels. In Proc. of the International Conference on Neural Networks '93, San Francisco. IEEE, 1993.
[FWPMETA5] J. Schmidhuber. Habilitation thesis, TUM, 1993. PDF. A recurrent neural net with a selfreferential, selfreading, selfmodifying weight matrix can be found here.
[FWPMETA6] L. Kirsch and J. Schmidhuber. Meta Learning Backpropagation & Improving It. Advances in Neural Information Processing Systems (NeurIPS), 2021. Preprint arXiv:2012.14905 [cs.LG], 2020. See also tweet1 and tweet2.
[FWPMETA7] I. Schlag, T. Munkhdalai, J. Schmidhuber. Learning Associative Inference Using Fast Weight Memory. To appear at ICLR 2021. Report arXiv:2011.07831 [cs.AI], 2020.
[FWPMETA8] K. Irie, I. Schlag, R. Csordas, J. Schmidhuber. A Modern SelfReferential Weight Matrix That Learns to Modify Itself. International Conference on Machine Learning (ICML), 2022. Preprint: arXiv:2202.05780.
[FWPMETA9] L. Kirsch and J. Schmidhuber. SelfReferential Meta Learning. First Conference on Automated Machine Learning (LateBreaking Workshop), 2022.
[GM3] J. Schmidhuber (2003). Goedel Machines: SelfReferential Universal Problem Solvers Making Provably Optimal SelfImprovements. Preprint arXiv:cs/0309048 (2003). More.
[GM6] J. Schmidhuber (2006). Gödel machines: Fully SelfReferential Optimal Universal SelfImprovers. In B. Goertzel and C. Pennachin, eds.: Artificial General Intelligence, p. 199226, 2006. PDF.
[GM9] J. Schmidhuber (2009). Ultimate Cognition à la Gödel. Cognitive Computation 1(2):177193, 2009. PDF. More.
[G63] R. J Glauber (1963). Timedependent statistics of the Ising model. Journal of Mathematical Physics, 4(2):294307, 1963.
[GD'] C. Lemarechal. Cauchy and the Gradient Method. Doc Math Extra, pp. 251254, 2012.
[GD''] J. Hadamard. Memoire sur le probleme d'analyse relatif a Vequilibre des plaques elastiques encastrees. Memoires presentes par divers savants estrangers Ă l'Academie des Sciences de l'Institut de France, 33, 1908.
[GDa] Y. Z. Tsypkin (1966). Adaptation, training and selforganization automatic control systems, Avtomatika I Telemekhanika, 27, 2361. On gradient descentbased online learning for nonlinear systems.
[GDb] Y. Z. Tsypkin (1971). Adaptation and Learning in Automatic Systems, Academic Press, 1971. On gradient descentbased online learning for nonlinear systems.
[GD1] S. I. Amari (1967). A theory of adaptive pattern classifier, IEEE Trans, EC16, 279307 (Japanese version published in 1965). PDF. Probably the first paper on using stochastic gradient descent^{[STO5152]} for learning in multilayer neural networks (without specifying the specific gradient descent method now known as reverse mode of automatic differentiation or backpropagation^{[BP1]}).
[GD2] S. I. Amari (1968). Information Theory—Geometric Theory of Information, Kyoritsu Publ., 1968 (in Japanese). OCRbased PDF scan of pages 94135 (see pages 119120). Contains computer simulation results for a five layer network (with 2 modifiable layers) which learns internal representations to classify nonlinearily separable pattern classes. See also this tweet.
[GD2a] H. Saito (1967). Master's thesis, Graduate School of Engineering, Kyushu University, Japan. Implementation of Amari's 1967 stochastic gradient descent method for multilayer perceptrons.^{[GD1]} (S. Amari, personal communication, 2021.)
[GD3] S. I. Amari (1977). Neural Theory of Association and Concept Formation. Biological Cybernetics, vol. 26, p. 175185, 1977. See Section 3.1 on using gradient descent for learning in multilayer networks.
[GSR] H. Sak, A. Senior, K. Rao, F. Beaufays, J. Schalkwyk—Google Speech Team. Google voice search: faster and more accurate. Google Research Blog, Sep 2015, see also Aug 2015 Google's speech recognition based on CTC and LSTM.
[GSR15] Dramatic improvement of Google's speech recognition through LSTM: Alphr Technology, Jul 2015, or 9to5google, Jul 2015
[GSR19] Y. He, T. N. Sainath, R. Prabhavalkar, I. McGraw, R. Alvarez, D. Zhao, D. Rybach, A. Kannan, Y. Wu, R. Pang, Q. Liang, D. Bhatia, Y. Shangguan, B. Li, G. Pundak, K. Chai Sim, T. Bagby, S. Chang, K. Rao, A. Gruenstein. Streaming endtoend speech recognition for mobile devices. ICASSP 20192019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.
[GT16] Google's dramatically improved Google Translate of 2016 is based on LSTM, e.g., WIRED, Sep 2016, or siliconANGLE, Sep 2016
[GAN0] O. Niemitalo. A method for training artificial neural networks to generate missing data within a variable context. Blog post, Internet Archive, 2010. A blog post describing the basic ideas^{[AC][AC90, AC90b][AC20]} of GANs.
[GAN1] I. Goodfellow, J. PougetAbadie, M. Mirza, B. Xu, D. WardeFarley, S. Ozair, A. Courville, Y. Bengio. Generative adversarial nets. NIPS 2014, 26722680, Dec 2014. A description of GANs that does not cite Schmidhuber's original GAN principle of 1990^{[AC][AC90,AC90b][AC20][R2][T22]} (also containing wrong claims about Schmidhuber's adversarial NNs for Predictability Minimization^{[PM02][AC20][T22]}).
[GAN2] T. Karras, S. Laine, T. Aila. A stylebased generator architecture for generative adversarial networks. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), pages 44014410, 2019.
[GF1] The Godfather (1972). Movie directed by Francis Ford Coppola, based on Mario Puzo's 1969 novel.
[GF2] T. Ranosa. Godfathers Of AI Win This Year's Turing Award And $1 Million. Tech Times, 29 March 2019.
[GOD] K. Gödel. Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I. Monatshefte für Mathematik und Physik, 38:173198, 1931. In the early 1930s, Gödel founded theoretical computer science. He identified fundamental limits of mathematics and theorem proving and computing and Artificial Intelligence.
[GOD34] K. Gödel (1934). On undecidable propositions of formal mathematical systems. Notes by S. C. Kleene and J. B. Rosser on lectures at the Institute for Advanced Study, Princeton, New Jersey, 1934, 30 pp. (Reprinted in M. Davis, (ed.), The Undecidable. Basic Papers on Undecidable Propositions, Unsolvable Problems, and Computable Functions, Raven Press, Hewlett, New York, 1965.) Gödel introduced a universal coding language.
[GOD56] R. J. Lipton and K. W. Regan. Gödel's lost letter and P=NP. Link.
[GOD86] K. Gödel. Collected works Volume I: Publications 192936, S. Feferman et. al., editors, Oxford Univ. Press, Oxford, 1986.
[GOD21] J. Schmidhuber (2021). 90th anniversary celebrations: 1931: Kurt Gödel, founder of theoretical computer science, shows limits of math, logic, computing, and artificial intelligence. This was number 1 on Hacker News.
[GOD21a]
J. Schmidhuber (2021). Als Kurt Gödel die Grenzen des Berechenbaren entdeckte.
(When Kurt Gödel discovered the limits of computability.)
Frankfurter Allgemeine Zeitung, 16/6/2021.
[GOL] C. Goller & A. Küchler (1996). Learning taskdependent distributed representations by backpropagation through structure. Proceedings of International Conference on Neural Networks (ICNN'96). Vol. 1, p. 347352 IEEE, 1996. Based on TR AR9502, TU Munich, 1995.
[GPT3] T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. HerbertVoss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, D. Amodei. Language Models are FewShot Learners (2020). Preprint arXiv/2005.14165.
[GPUNN] Oh, K.S. and Jung, K. (2004). GPU implementation of neural networks. Pattern Recognition, 37(6):13111314. Speeding up traditional NNs on GPU by a factor of 20.
[GPUCNN] K. Chellapilla, S. Puri, P. Simard. High performance convolutional neural networks for document processing. International Workshop on Frontiers in Handwriting Recognition, 2006. Speeding up shallow CNNs on GPU by a factor of 4.
[GPUCNN1] D. C. Ciresan, U. Meier, J. Masci, L. M. Gambardella, J. Schmidhuber. Flexible, High Performance Convolutional Neural Networks for Image Classification. International Joint Conference on Artificial Intelligence (IJCAI2011, Barcelona), 2011. PDF. ArXiv preprint. Speeding up deep CNNs on GPU by a factor of 60. Used to win four important computer vision competitions 20112012 before others won any with similar approaches.
[GPUCNN2] D. C. Ciresan, U. Meier, J. Masci, J. Schmidhuber. A Committee of Neural Networks for Traffic Sign Classification. International Joint Conference on Neural Networks (IJCNN2011, San Francisco), 2011. PDF. HTML overview. First superhuman performance in a computer vision contest, with half the error rate of humans, and one third the error rate of the closest competitor.^{[DAN1]} This led to massive interest from industry.
[GPUCNN3] D. C. Ciresan, U. Meier, J. Schmidhuber. Multicolumn Deep Neural Networks for Image Classification. Proc. IEEE Conf. on Computer Vision and Pattern Recognition CVPR 2012, p 36423649, July 2012. PDF. Longer TR of Feb 2012: arXiv:1202.2745v1 [cs.CV]. More.
[GPUCNN4] A. Krizhevsky, I. Sutskever, G. E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. NIPS 25, MIT Press, Dec 2012. PDF. This paper describes AlexNet, which is similar to the earlier DanNet,^{[DAN,DAN1][R6]} the first pure deep CNN to win computer vision contests in 2011^{[GPUCNN23,5]} (AlexNet and VGG Net^{[GPUCNN9]} followed in 20122014). [GPUCNN4] emphasizes benefits of Fukushima's ReLUs (1969)^{[RELU1]} and dropout (a variant of Hanson 1990 stochastic delta rule)^{[Drop14]} but neither cites the original work^{[RELU1][Drop1]} nor the basic CNN architecture (Fukushima, 1979).^{[CNN1]}
[GPUCNN5] J. Schmidhuber (AI Blog, 2017; updated 2021 for 10th birthday of DanNet): History of computer vision contests won by deep CNNs since 2011. DanNet won 4 of them in a row before the similar AlexNet/VGG Net and the Resnet (a Highway Net with open gates) joined the party. Today, deep CNNs are standard in computer vision.
[GPUCNN6] J. Schmidhuber, D. Ciresan, U. Meier, J. Masci, A. Graves. On Fast Deep Nets for AGI Vision. In Proc. Fourth Conference on Artificial General Intelligence (AGI11), Google, Mountain View, California, 2011. PDF.
[GPUCNN7] D. C. Ciresan, A. Giusti, L. M. Gambardella, J. Schmidhuber. Mitosis Detection in Breast Cancer Histology Images using Deep Neural Networks. MICCAI 2013. PDF.
[GPUCNN8] J. Schmidhuber (AI Blog, 2017; updated 2021 for 10th birthday of DanNet). First deep learner to win a contest on object detection in large images— first deep learner to win a medical imaging contest (2012). Link. How the Swiss AI Lab IDSIA used GPUbased CNNs to win the ICPR 2012 Contest on Mitosis Detection and the MICCAI 2013 Grand Challenge.
[GPUCNN9] K. Simonyan, A. Zisserman. Very deep convolutional networks for largescale image recognition. Preprint arXiv:1409.1556 (2014).
[H86] J. L. van Hemmen (1986). Spinglass models of a neural network. Phys. Rev. A 34, 3435, 1 Oct 1986.
[H88] H. Sompolinsky (1988). Statistical Mechanics of Neural Networks. Physics Today 41, 12, 70, 1988.
[H90] W. D. Hillis. Coevolving parasites improve simulated evolution as an optimization procedure. Physica D: Nonlinear Phenomena, 42(13):228234, 1990.
[HB96] S. El Hihi, Y. Bengio. Hierarchical recurrent neural networks for longterm dependencies. NIPS, 1996. Bengio claimed^{[YB20]} that in 1995 he "introduced the use of a hierarchy of time scales to combat the vanishing gradients issue" although Schmidhuber's publications on exactly this topic date back to 199193.^{[UN02][UN]}
[HEL] P. Dayan, G. E. Hinton, R. M. Neal, and R. S. Zemel. The Helmholtz machine. Neural Computation, 7:889904, 1995. An unsupervised learning algorithm related to Schmidhuber's supervised Neural Heat Exchanger.^{[NHE]}
[HIN] J. Schmidhuber (AI Blog, 2020). Critique of Honda Prize for Dr. Hinton. Science must not allow corporate PR to distort the academic record. See also this tweet.
[HIN22] G. Hinton. The ForwardForward Algorithm: Some Preliminary Investigations. Preprint, Google Brain, 2022. Proposal of a biologically more plausible deep learning algorithm that—unlike backpropagation—is local in space and time. Does not mention previous related work.^{[BB2][NAN14][NHE][MIR](Sec. 15, Sec. 17)[FWPMETA6]}
[HO07] S. Hochreiter, M. Heusel, K. Obermayer. Fast modelbased protein homology detection without alignment. Bioinformatics 23(14):172836, 2007. Successful application of deep learning to protein folding problems, through an LSTM that was orders of magnitude faster than competing methods.
[HRL0] J. Schmidhuber. Towards compositional learning with dynamic neural networks. Technical Report FKI12990, Institut für Informatik, Technische Universität München, 1990. PDF. An RL machine gets extra command inputs of the form (start, goal). An evaluator NN learns to predict the current rewards/costs of going from start to goal. An (R)NNbased subgoal generator also sees (start, goal), and uses (copies of) the evaluator NN to learn by gradient descent a sequence of costminimising intermediate subgoals. The RL machine tries to use such subgoal sequences to achieve final goals. The system is learning action plans at multiple levels of abstraction and multiple time scales and solves what Y. LeCun called an "open problem" in 2022.^{[LEC]} See also this tweet.
[HRL1] J. Schmidhuber. Learning to generate subgoals for action sequences. In T. Kohonen, K. Mäkisara, O. Simula, and J. Kangas, editors, Artificial Neural Networks, pages 967972. Elsevier Science Publishers B.V., NorthHolland, 1991. PDF. Extending TR FKI12990, TUM, 1990 [HRL0].
[HRL2] J. Schmidhuber and R. Wahnsiedler. Planning simple trajectories using neural subgoal generators. In J. A. Meyer, H. L. Roitblat, and S. W. Wilson, editors, Proc. of the 2nd International Conference on Simulation of Adaptive Behavior, pages 196202. MIT Press, 1992. PDF. (See also HTML & images in German.)
[HRL3] P. Dayan and G. E. Hinton. Feudal Reinforcement Learning. Advances in Neural Information Processing Systems 5, NIPS, 1992. This work did not cite Schmidhuber's gradientbased subgoal generators for hierarchical reinforcement learning (1990).^{[HRL02]}
[HRL4] M. Wiering and J. Schmidhuber. HQLearning. Adaptive Behavior 6(2):219246, 1997. PDF.
[HRLW] C. Watkins (1989). Learning from delayed rewards.
[HW] J. Schmidhuber (AI Blog, 2015, updated 2020 for 5year anniversary). Overview of Highway Networks: First working really deep feedforward neural networks with over 100 layers (previous NNs had at most a few tens of layers).
[HW1] R. K. Srivastava, K. Greff, J. Schmidhuber. Highway networks. Preprints arXiv:1505.00387 (May 2015) and arXiv:1507.06228 (July 2015). Also at NIPS 2015. The first working very deep feedforward nets with over 100 layers (previous NNs had at most a few tens of layers). Let g, t, h, denote nonlinear differentiable functions. Each noninput layer of a highway net computes g(x)x + t(x)h(x), where x is the data from the previous layer. (Like LSTM with forget gates^{[LSTM2]} for RNNs.) Resnets^{[HW2]} are a version of this where the gates are always open: g(x)=t(x)=const=1. Highway Nets perform roughly as well as ResNets^{[HW2]} on ImageNet.^{[HW3]} Variants of highway gates are also used for certain algorithmic tasks, where the simpler residual layers do not work as well.^{[NDR]} More.
[HW1a] R. K. Srivastava, K. Greff, J. Schmidhuber. Highway networks. Presentation at the Deep Learning Workshop, ICML'15, July 1011, 2015. Link.
[HW2] He, K., Zhang, X., Ren, S., Sun, J. Deep residual learning for image recognition. Preprint arXiv:1512.03385 (Dec 2015). Residual nets are a version of Highway Nets^{[HW1]} where the gates are always open: g(x)=1 (a typical highway net initialization) and t(x)=1. More.
[HW3] K. Greff, R. K. Srivastava, J. Schmidhuber. Highway and Residual Networks learn Unrolled Iterative Estimation. Preprint arxiv:1612.07771 (2016). Also at ICLR 2017.
[HYB12] Hinton, G. E., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., and Kingsbury, B. (2012). Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Process. Mag., 29(6):8297. This work did not cite the earlier LSTM^{[LSTM06]} trained by Connectionist Temporal Classification (CTC, 2006).^{[CTC]} CTCLSTM was successfully applied to speech in 2007^{[LSTM4]} (also with hierarchical LSTM stacks^{[LSTM14]}) and became the first superior endtoend neural speech recogniser that outperformed the state of the art, dramatically improving Google's speech recognition.^{[GSR][GSR15][DL4]} This was very different from previous hybrid methods since the late 1980s which combined NNs and traditional approaches such as hidden Markov models (HMMs).^{[BW][BRI][BOU]} [HYB12] still used the old hybrid approach and did not compare it to CTCLSTM. Later, however, Hinton switched to LSTM, too.^{[LSTM8]}
[I24] E. Ising (1925). Beitrag zur Theorie des Ferro und Paramagnetismus. Dissertation, 1924.
[I25] E. Ising (1925). Beitrag zur Theorie des Ferromagnetismus. Z. Phys., 31 (1): 253258, 1925. The first nonlearning recurrent NN architecture (the Ising model or LenzIsing model) was introduced and analyzed by physicists Ernst Ising and Wilhelm Lenz in the 1920s.^{[L20][I25][K41][W45][T22]} It settles into an equilibrium state in response to input conditions, and is the foundation of the first published learning RNNs.^{[AMH12]}
[IM09] J. Deng, R. Socher, L.J. Li, K. Li, L. FeiFei (2009). Imagenet: A largescale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pp. 248255). IEEE, 2009.
[JOU17] Jouppi et al. (2017). InDatacenter Performance Analysis of a Tensor Processing Unit. Preprint arXiv:1704.04760
[K41] H. A. Kramers and G. H. Wannier (1941). Statistics of the TwoDimensional Ferromagnet. Phys. Rev. 60, 252 and 263, 1941.
[K56] S.C. Kleene. Representation of Events in Nerve Nets and Finite Automata. Automata Studies, Editors: C.E. Shannon and J. McCarthy, Princeton University Press, p. 342, Princeton, N.J., 1956.
[KO2] J. Schmidhuber. Discovering neural nets with low Kolmogorov complexity and high generalization capability. Neural Networks, 10(5):857873, 1997. PDF.
[KU] A. Küchler & C. Goller (1996). Inductive learning in symbolic domains using structuredriven recurrent neural networks. Lecture Notes in Artificial Intelligence, vol 1137. Springer, Berlin, Heidelberg.
[L20] W. Lenz (1920). Beitrag zum Verständnis der magnetischen Erscheinungen in festen Körpern. Physikalische Zeitschrift, 21:613615. See also [I25].
[LAN] J. L. Ba, J. R.Kiros, G. E. Hinton. Layer Normalization. arXiv:1607.06450, 2016.
[LEC] J. Schmidhuber (AI Blog, 2022). LeCun's 2022 paper on autonomous machine intelligence rehashes but does not cite essential work of 19902015. Years ago, Schmidhuber's team published most of what Y. LeCun calls his "main original contributions:" neural nets that learn multiple time scales and levels of abstraction, generate subgoals, use intrinsic motivation to improve world models, and plan (1990); controllers that learn informative predictable representations (1997), etc. This was also discussed on Hacker News, reddit, and in the media. See tweet1. LeCun also listed the "5 best ideas 20122022" without mentioning that most of them are from Schmidhuber's lab, and older. See tweet2.
[LEC22a] Y. LeCun (27 June 2022). A Path Towards Autonomous Machine Intelligence. OpenReview Archive. Link. See critique [LEC].
[LEC22b] M. Heikkilä, W. D. Heaven. Yann LeCun has a bold new vision for the future of AI. MIT Technology Review, 24 June 2022. Link. See critique [LEC].
[LEC22c] ZDNet, 2022. Meta's AI guru LeCun: Most of today's AI approaches will never lead to true intelligence. Here LeCun makes wrong and misleading claims about Schmidhuber's work, as discussed in Addendum III of [LEC].
[LEC22d] Analytics India, Dec 2022. Angels & Demons of AI. More of LeCun's misleading statements about the disputes with Schmidhuber, as discussed in of [LEC].
[LECP] Y. LeCun. A New Publishing Model in Computer Science. Pamphlet, 20002004. Local copy (HTML only).
[LEI07] J. M. Child (translator), G. W. Leibniz (Author). The Early Mathematical Manuscripts of Leibniz. Merchant Books, 2007. See p. 126: the chain rule appeared in a 1676 memoir by Leibniz.
[LEI10] O. H. Rodriguez, J. M. Lopez Fernandez (2010). A semiotic reflection on the didactics of the Chain rule. The Mathematics Enthusiast: Vol. 7 : No. 2 , Article 10. DOI: https://doi.org/10.54870/15513440.1191.
[LEI21] J. Schmidhuber (AI Blog, 2021). 375th birthday of Leibniz, founder of computer science.
[LEI21a] J. Schmidhuber (2021). Der erste Informatiker. Wie Gottfried Wilhelm Leibniz den Computer erdachte. (The first computer scientist. How Gottfried Wilhelm Leibniz conceived the computer.) Frankfurter Allgemeine Zeitung (FAZ), 17/5/2021. FAZ online: 19/5/2021.
[LIT21] M. L. Littman (2021). Collusion Rings Threaten the Integrity of Computer Science Research. Communications of the ACM, Vol. 64 No. 6, p. 4344, June 2021.
[LSTM0] S. Hochreiter and J. Schmidhuber. Long ShortTerm Memory. TR FKI20795, TUM, August 1995. PDF.
[LSTM1] S. Hochreiter, J. Schmidhuber. Long ShortTerm Memory. Neural Computation, 9(8):17351780, 1997. PDF. Based on [LSTM0]. More.
[LSTM2] F. A. Gers, J. Schmidhuber, F. Cummins. Learning to Forget: Continual Prediction with LSTM. Neural Computation, 12(10):24512471, 2000. PDF. The "vanilla LSTM architecture" with forget gates that everybody is using today, e.g., in Google's Tensorflow.
[LSTM3] A. Graves, J. Schmidhuber. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Networks, 18:56, pp. 602610, 2005. PDF.
[LSTM4] S. Fernandez, A. Graves, J. Schmidhuber. An application of recurrent neural networks to discriminative keyword spotting. Intl. Conf. on Artificial Neural Networks ICANN'07, 2007. PDF.
[LSTM5] A. Graves, M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, J. Schmidhuber. A Novel Connectionist System for Improved Unconstrained Handwriting Recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 5, 2009. PDF.
[LSTM6] A. Graves, J. Schmidhuber. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks. NIPS'22, p 545552, Vancouver, MIT Press, 2009. PDF.
[LSTM7] J. Bayer, D. Wierstra, J. Togelius, J. Schmidhuber. Evolving memory cell structures for sequence learning. Proc. ICANN09, Cyprus, 2009. PDF.
[LSTM8] A. Graves, A. Mohamed, G. E. Hinton. Speech Recognition with Deep Recurrent Neural Networks. ICASSP 2013, Vancouver, 2013. PDF. Based on [LSTM12,4,14][CTC].
[LSTM9] O. Vinyals, L. Kaiser, T. Koo, S. Petrov, I. Sutskever, G. Hinton. Grammar as a Foreign Language. Preprint arXiv:1412.7449 [cs.CL].
[LSTM10] A. Graves, D. Eck and N. Beringer, J. Schmidhuber. Biologically Plausible Speech Recognition with LSTM Neural Nets. In J. Ijspeert (Ed.), First Intl. Workshop on Biologically Inspired Approaches to Advanced Information Technology, BioADIT 2004, Lausanne, Switzerland, p. 175184, 2004. PDF.
[LSTM11] N. Beringer and A. Graves and F. Schiel and J. Schmidhuber. Classifying unprompted speech by retraining LSTM Nets. In W. Duch et al. (Eds.): Proc. Intl. Conf. on Artificial Neural Networks ICANN'05, LNCS 3696, pp. 575581, SpringerVerlag Berlin Heidelberg, 2005.
[LSTM12] D. Wierstra, F. Gomez, J. Schmidhuber. Modeling systems with internal state using Evolino. In Proc. of the 2005 conference on genetic and evolutionary computation (GECCO), Washington, D. C., pp. 17951802, ACM Press, New York, NY, USA, 2005. Got a GECCO best paper award.
[LSTM13] F. A. Gers and J. Schmidhuber. LSTM Recurrent Networks Learn Simple Context Free and Context Sensitive Languages. IEEE Transactions on Neural Networks 12(6):13331340, 2001. PDF.
[LSTM14] S. Fernandez, A. Graves, J. Schmidhuber. Sequence labelling in structured domains with hierarchical recurrent neural networks. In Proc. IJCAI 07, p. 774779, Hyderabad, India, 2007 (talk). PDF.
[LSTM15] A. Graves, J. Schmidhuber. Offline Handwriting Recognition with Multidimensional Recurrent Neural Networks. Advances in Neural Information Processing Systems 22, NIPS'22, p 545552, Vancouver, MIT Press, 2009. PDF.
[LSTM16] M. Stollenga, W. Byeon, M. Liwicki, J. Schmidhuber. Parallel MultiDimensional LSTM, With Application to Fast Biomedical Volumetric Image Segmentation. Advances in Neural Information Processing Systems (NIPS), 2015. Preprint: arxiv:1506.07452.
[LSTM17] J. A. PerezOrtiz, F. A. Gers, D. Eck, J. Schmidhuber. Kalman filters improve LSTM network performance in problems unsolvable by traditional recurrent nets. Neural Networks 16(2):241250, 2003. PDF.
[LSTMPG] J. Schmidhuber (AI Blog, Dec 2020). 10year anniversary of our journal paper on deep reinforcement learning with policy gradients for LSTM (20072010). Recent famous applications: DeepMind's Starcraft player (2019) and OpenAI's dextrous robot hand & Dota player (2018)—Bill Gates called this a huge milestone in advancing AI.
[LSTMRL] B. Bakker, F. Linaker, J. Schmidhuber. Reinforcement Learning in Partially Observable Mobile Robot Domains Using Unsupervised Event Extraction. In Proceedings of the 2002 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2002), Lausanne, 2002. PDF.
[LSTMGRU] J. Chung, C. Gulcehre, K. Cho, Y. Bengio (2014). Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. Preprint arXiv:1412.3555 [cs.NE]. The socalled gated recurrent units (GRU) are actually a variant of the vanilla LSTM architecture^{[LSTM2]} (2000) which the authors did not cite although this work^{[LSTM2]} was the one that introduced gated recurrent units. They cited only the 1997 LSTM^{[LSTM1]} which did not yet have "forget gates."^{[LSTM2]} Furthermore, Schmidhuber's team automatically evolved lots of additional LSTM variants and topologies already in 2009^{[LSTM7]} without changing the name of the basic method. (Margin note: GRU cells lack an important gate and can neither learn to count^{[LSTMGRU2]} nor learn simple nonregular languages;^{[LSTMGRU2]} they also do not work as well for challenging translation tasks, according to Google Brain.^{[LSTMGRU3]})
[LSTMGRU2] G. Weiss, Y. Goldberg, E. Yahav. On the Practical Computational Power of Finite Precision RNNs for Language Recognition. Preprint arXiv:1805.04908.
[LSTMGRU3] D. Britz et al. (2017). Massive Exploration of Neural Machine Translation Architectures. Preprint arXiv:1703.03906
[M69] M. Minsky, S. Papert. Perceptrons (MIT Press, Cambridge, MA, 1969). A misleading "history of deep learning" goes more or less like this: "In 1969, Minsky & Papert^{[M69]} showed that shallow NNs without hidden layers are very limited and the field was abandoned until a new generation of neural network researchers took a fresh look at the problem in the 1980s."^{[S20]} However, the 1969 book^{[M69]} addressed a "problem" of Gauss & Legendre's shallow learning (~1800)^{[DL12]} that had already been solved 4 years prior by Ivakhnenko & Lapa's popular deep learning method,^{[DEEP12][DL2]} and then also by Amari's SGD for MLPs.^{[GD12]} Minsky was apparently unaware of this and failed to correct it later.^{[HIN](Sec. I)[T22](Sec. XIII)}
[MC43] W. S. McCulloch, W. Pitts. A Logical Calculus of Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics, Vol. 5, p. 115133, 1943.
[META] J. Schmidhuber (AI Blog, 2020). 1/3 century anniversary of first publication on metalearning machines that learn to learn (1987). For its cover I drew a robot that bootstraps itself. 1992: gradient descentbased neural metalearning. 1994: MetaReinforcement Learning with selfmodifying policies. 1997: MetaRL plus artificial curiosity and intrinsic motivation. 2002: asymptotically optimal metalearning for curriculum learning. 2003: mathematically optimal Gödel Machine. 2020: new stuff!
[META1] J. Schmidhuber. Evolutionary principles in selfreferential learning, or on learning how to learn: The metameta... hook. Diploma thesis, Institut für Informatik, Technische Universität München, 1987. Searchable PDF scan (created by OCRmypdf which uses LSTM). HTML. For example, Genetic Programming (GP) is applied to itself, to recursively evolve better GP methods through MetaEvolution. More.
[META10] T. Schaul and J. Schmidhuber. Metalearning. Scholarpedia, 5(6):4650, 2010.
[METARL2] J. Schmidhuber. On learning how to learn learning strategies. Technical Report FKI19894, Fakultät für Informatik, Technische Universität München, November 1994. PDF.
[METARL3] J. Schmidhuber. Beyond "Genetic Programming": Incremental SelfImprovement. In J. Rosca, ed., Proc. Workshop on Genetic Programming at ML95, pages 4249. National Resource Lab for the study of Brain and Behavior, 1995.
[METARL4] M. Wiering and J. Schmidhuber. Solving POMDPs using Levin search and EIRA. In L. Saitta, ed., Machine Learning: Proceedings of the 13th International Conference (ICML 1996), pages 534542, Morgan Kaufmann Publishers, San Francisco, CA, 1996. PDF. HTML.
[METARL5] J. Schmidhuber and J. Zhao and M. Wiering. Simple principles of metalearning. Technical Report IDSIA6996, IDSIA, June 1996. PDF.
[METARL6] J. Zhao and J. Schmidhuber. Solving a complex prisoner's dilemma with selfmodifying policies. In From Animals to Animats 5: Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior, 1998.
[METARL7] Shifting inductive bias with successstory algorithm, adaptive Levin search, and incremental selfimprovement. Machine Learning 28:105130, 1997. PDF.
[METARL8] J. Schmidhuber, J. Zhao, N. Schraudolph. Reinforcement learning with selfmodifying policies. In S. Thrun and L. Pratt, eds., Learning to learn, Kluwer, pages 293309, 1997. PDF; HTML.
[METARL9] A general method for incremental selfimprovement and multiagent learning. In X. Yao, editor, Evolutionary Computation: Theory and Applications. Chapter 3, pp.81123, Scientific Publ. Co., Singapore, 1999.
[METARL10] L. Kirsch, S. van Steenkiste, J. Schmidhuber. Improving Generalization in Meta Reinforcement Learning using Neural Objectives. International Conference on Learning Representations, 2020.
[MGC] MICCAI 2013 Grand Challenge on Mitosis Detection, organised by M. Veta, M.A. Viergever, J.P.W. Pluim, N. Stathonikos, P. J. van Diest of University Medical Center Utrecht.
[MIR] J. Schmidhuber (AI Blog, Oct 2019, updated 2021, 2022). Deep Learning: Our Miraculous Year 19901991. Preprint arXiv:2005.05744, 2020. The deep learning neural networks of Schmidhuber's team have revolutionised pattern recognition and machine learning, and are now heavily used in academia and industry. In 202021, we celebrate that many of the basic ideas behind this revolution were published within fewer than 12 months in the "Annus Mirabilis" 19901991 at TU Munich. See also this inaugural tweet.
[MLP1] D. C. Ciresan, U. Meier, L. M. Gambardella, J. Schmidhuber. Deep Big Simple Neural Nets For Handwritten Digit Recognition. Neural Computation 22(12): 32073220, 2010. ArXiv Preprint. Showed that plain backprop for deep standard NNs is sufficient to break benchmark records, without any unsupervised pretraining.
[MLP2] J. Schmidhuber (AI Blog, Sep 2020). 10year anniversary of supervised deep learning breakthrough (2010). No unsupervised pretraining. By 2010, when compute was 100 times more expensive than today, both the feedforward NNs^{[MLP1]} and the earlier recurrent NNs of Schmidhuber's team were able to beat all competing algorithms on important problems of that time. This deep learning revolution quickly spread from Europe to North America and Asia. The rest is history.
[MOST] J. Schmidhuber (AI Blog, 2021). The most cited neural networks all build on work done in my labs. Foundations of the most popular NNs originated in Schmidhuber's labs at TU Munich and IDSIA. (1) Long ShortTerm Memory (LSTM), the most cited AI of the 20th century. (2) ResNet (opengated Highway Net), the most cited AI of the 21st century. (3) AlexNet & VGG Net (the similar DanNet of 2011 won 4 image recognition challenges before them). (4) GAN (an instance of Schmidhuber's Adversarial Artificial Curiosity of 1990). (5) Transformer variants (unnormalised linear Transformers are formally equivalent to Schmidhuber's Fast Weight Programmers of 1991). In particular, Schmidhuber laid foundations of Generative AI, publishing principles of (4) GANs (1990, now used for deepfakes), (5) Transformers (1991, the "T" in "ChatGPT" stands for "Transformer"), and (6) selfsupervised pretraining for deep NNs (the "P" in "GPT" stands for "pretrained"). Most of this started with the Annus Mirabilis of 19901991.^{[MIR]}
[MOZ] M. Mozer. A Focused Backpropagation Algorithm for Temporal Pattern Recognition. Complex Systems, 1989.
[MUN87] P. W. Munro. A dual backpropagation scheme for scalar reinforcement learning. Proceedings of the Ninth Annual Conference of the Cognitive Science Society, Seattle, WA, pages 165176, 1987.
[NAN1] J. Schmidhuber. Networks adjusting networks. In J. Kindermann and A. Linden, editors, Proceedings of `Distributed Adaptive Neural Information Processing', St.Augustin, 24.25.5. 1989, pages 197208. Oldenbourg, 1990. Extended version: TR FKI12590 (revised), Institut für Informatik, TUM. PDF. Includes the proposal of a biologically more plausible deep learning algorithm that—unlike backpropagation—is local in space and time. Based on neural nets learning to estimate gradients for other neural nets.
[NAN2] J. Schmidhuber. Networks adjusting networks. Technical Report FKI12590, Institut für Informatik, Technische Universität München. Revised in November 1990. PDF.
[NAN3] Recurrent networks adjusted by adaptive critics. In Proc. IEEE/INNS International Joint Conference on Neural Networks, Washington, D. C., volume 1, pages 719722, 1990.
[NAN4] J. Schmidhuber. Additional remarks on G. Lukes' review of Schmidhuber's paper `Recurrent networks adjusted by adaptive critics'. Neural Network Reviews, 4(1):43, 1990.
[NAN5] M. Jaderberg, W. M. Czarnecki, S. Osindero, O. Vinyals, A. Graves, D. Silver, K. Kavukcuoglu. Decoupled Neural Interfaces using Synthetic Gradients. Preprint arXiv:1608.05343, 2016. This work of DeepMind is similar to [NAN12].
[NAS] B. Zoph, Q. V. Le. Neural Architecture Search with Reinforcement Learning. Preprint arXiv:1611.01578 (PDF), 2017.
[NASC1] J. Schmidhuber. First Pow(d)ered flight / plane truth. Correspondence, Nature, 421 p 689, Feb 2003.
[NASC2] J. Schmidhuber. Zooming in on aviation history. Correspondence, Nature, vol 566, p 39, 7 Feb 2019.
[NASC3] J. Schmidhuber. The last inventor of the telephone. Letter, Science, 319, no. 5871, p. 1759, March 2008.
[NASC4] J. Schmidhuber. Turing: Keep his work in perspective. Correspondence, Nature, vol 483, p 541, March 2012, doi:10.1038/483541b.
[NASC5] J. Schmidhuber. Turing in Context. Letter, Science, vol 336, p 1639, June 2012. (On Gödel, Zuse, Turing.) See also comment on response by A. Hodges (DOI:10.1126/science.336.6089.1639a)
[NASC6] J. Schmidhuber. Colossus was the first electronic digital computer. Correspondence, Nature, 441 p 25, May 2006.
[NASC7] J. Schmidhuber. Turing's impact. Correspondence, Nature, 429 p 501, June 2004
[NASC8] J. Schmidhuber. Prototype resilient, selfmodeling robots. Correspondence, Science, 316, no. 5825 p 688, May 2007.
[NASC9] J. Schmidhuber. Comparing the legacies of Gauss, Pasteur, Darwin. Correspondence, Nature, vol 452, p 530, April 2008.
[NAT1] J. Schmidhuber. Citation bubble about to burst? Nature, vol. 469, p. 34, 6 January 2011. HTML.
[NDR] R. Csordas, K. Irie, J. Schmidhuber. The Neural Data Router: Adaptive Control Flow in Transformers Improves Systematic Generalization. Proc. ICLR 2022. Preprint arXiv/2110.07732.
[NGU89] D. Nguyen and B. Widrow; The truck backerupper: An example of self learning in neural networks. In IEEE/INNS International Joint Conference on Neural Networks, Washington, D.C., volume 1, pages 357364, 1989.
[NHE] J. Schmidhuber. The Neural Heat Exchanger. Oral presentations since 1990 at various universities including TUM and the University of Colorado at Boulder. Also in In S. Amari, L. Xu, L. Chan, I. King, K. Leung, eds., Proceedings of the Intl. Conference on Neural Information Processing (1996), pages 194197, Springer, Hongkong. Link. Proposal of a biologically more plausible deep learning algorithm that—unlike backpropagation—is local in space and time. Inspired by the physical heat exchanger: inputs "heat up" while being transformed through many successive layers, targets enter from the other end of the deep pipeline and "cool down."
[NPMa] M. Nakamura, K. Shikano. A study of English word category prediction based on neural networks. Proc. IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP), p. 731734, 1989.
[NPM] Y. Bengio, R. Ducharme, P. Vincent, C. Jauvin (2003). A Neural Probabilistic Language Model. Journal of Machine Learning Research 3, p 11371155, 2003. Based on Schmidhuber & Heil's excellent 1995 neural probabilistic text model.^{[SNT]} See also Nakamura and Shikano's 1989 word category prediction model.^{[NPMa]}
[NS56] A. Newell and H. Simon. The logic theory machine—A complex information processing system. IRE Transactions on Information Theory 2.3 (1956):6179.
[NYT1] NY Times article by J. Markoff, Nov. 27, 2016: When A.I. Matures, It May Call Jürgen Schmidhuber 'Dad'
[NYT3] NY Times article by G. LewisKraus, Dec. 14, 2016: The Great A.I. Awakening
[OAI1] G. Powell, J. Schneider, J. Tobin, W. Zaremba, A. Petron, M. Chociej, L. Weng, B. McGrew, S. Sidor, A. Ray, P. Welinder, R. Jozefowicz, M. Plappert, J. Pachocki, M. Andrychowicz, B. Baker. Learning Dexterity. OpenAI Blog, 2018.
[OAI1a] OpenAI, M. Andrychowicz, B. Baker, M. Chociej, R. Jozefowicz, B. McGrew, J. Pachocki, A. Petron, M. Plappert, G. Powell, A. Ray, J. Schneider, S. Sidor, J. Tobin, P. Welinder, L. Weng, W. Zaremba. Learning Dexterous InHand Manipulation. arxiv:1312.5602 (PDF).
[OAI2] OpenAI: C. Berner, G. Brockman, B. Chan, V. Cheung, P. Debiak, C. Dennison, D. Farhi, Q. Fischer, S. Hashme, C. Hesse, R. Jozefowicz, S. Gray, C. Olsson, J. Pachocki, M. Petrov, H. P. de Oliveira Pinto, J. Raiman, T. Salimans, J. Schlatter, J. Schneider, S. Sidor, I. Sutskever, J. Tang, F. Wolski, S. Zhang (Dec 2019). Dota 2 with Large Scale Deep Reinforcement Learning. Preprint arxiv:1912.06680. An LSTM composes 84% of the model's total parameter count.
[OAI2a] J. Rodriguez. The Science Behind OpenAI Five that just Produced One of the Greatest Breakthrough in the History of AI. Towards Data Science, 2018. An LSTM with 84% of the model's total parameter count was the core of OpenAI Five.
[PDA1] G.Z. Sun, H.H. Chen, C.L. Giles, Y.C. Lee, D. Chen. Neural Networks with External Memory Stack that Learn Context—Free Grammars from Examples. Proceedings of the 1990 Conference on Information Science and Systems, Vol.II, pp. 649653, Princeton University, Princeton, NJ, 1990.
[PDA2] M. Mozer, S. Das. A connectionist symbol manipulator that discovers the structure of contextfree languages. Proc. NIPS 1993.
[PG] R. J. Williams. Simple statistical gradientfollowing algorithms for connectionist reinforcement learning. Machine Learning 8.34: 229256, 1992.
[PHD] J. Schmidhuber. Dynamische neuronale Netze und das fundamentale raumzeitliche Lernproblem (Dynamic neural nets and the fundamental spatiotemporal credit assignment problem). Dissertation, Institut für Informatik, Technische Universität München, 1990. PDF. HTML.
[PLAG1] Oxford's guide to types of plagiarism (2021). Quote: "Plagiarism may be intentional or reckless, or unintentional." Copy in the Internet Archive. Local copy.
[PLAG2] Jackson State Community College (2022). Unintentional Plagiarism. Copy in the Internet Archive.
[PLAG3] R. L. Foster. Avoiding Unintentional Plagiarism. Journal for Specialists in Pediatric Nursing; Hoboken Vol. 12, Iss. 1, 2007.
[PLAG4] N. Das. Intentional or unintentional, it is never alright to plagiarize: A note on how Indian universities are advised to handle plagiarism. Perspect Clin Res 9:567, 2018.
[PLAG5] InfoSciOnDemand (2023). What is Unintentional Plagiarism? Copy in the Internet Archive.
[PLAG6] Copyrighted.com (2022). How to Avoid Accidental and Unintentional Plagiarism (2023). Copy in the Internet Archive. Quote: "May it be accidental or intentional, plagiarism is still plagiarism."
[PLAN] J. Schmidhuber (AI Blog, 2020). 30year anniversary of planning & reinforcement learning with recurrent world models and artificial curiosity (1990). This work also introduced highdimensional reward signals, deterministic policy gradients for RNNs, the GAN principle (widely used today). Agents with adaptive recurrent world models even suggest a simple explanation of consciousness & selfawareness.
[PLAN2] J. Schmidhuber. An online algorithm for dynamic reinforcement learning and planning in reactive environments. Proc. IEEE/INNS International Joint Conference on Neural Networks, San Diego, volume 2, pages 253258, 1990. Based on TR FKI12690 (1990).^{[AC90]} More. Extending NNbased system identification and control of the 1980s by Werbos, Munro, Nguyen & Widrow, and others.
[PLAN3] J. Schmidhuber. Reinforcement learning in Markovian and nonMarkovian environments. In D. S. Lippman, J. E. Moody, and D. S. Touretzky, editors, Advances in Neural Information Processing Systems 3, NIPS'3, pages 500506. San Mateo, CA: Morgan Kaufmann, 1991. PDF. Partially based on TR FKI12690 (1990).^{[AC90]}
[PLAN4] J. Schmidhuber. On Learning to Think: Algorithmic Information Theory for Novel Combinations of Reinforcement Learning Controllers and Recurrent Neural World Models. Report arXiv:1210.0118 [cs.AI], 2015.
[PLAN5] One Big Net For Everything. Preprint arXiv:1802.08864 [cs.AI], Feb 2018.
[PLAN6] D. Ha, J. Schmidhuber. Recurrent World Models Facilitate Policy Evolution. Advances in Neural Information Processing Systems (NIPS), Montreal, 2018. (Talk.) Preprint: arXiv:1809.01999. Github: World Models.
[PM0] J. Schmidhuber. Learning factorial codes by predictability minimization. TR CUCS56591, Univ. Colorado at Boulder, 1991. PDF. More.
[PM1] J. Schmidhuber. Learning factorial codes by predictability minimization. Neural Computation, 4(6):863879, 1992. Based on [PM0], 1991. PDF. More.
[PM2] J. Schmidhuber, M. Eldracher, B. Foltin. Semilinear predictability minimzation produces wellknown feature detectors. Neural Computation, 8(4):773786, 1996. PDF. More.
[PMax0] J. Schmidhuber and D. Prelinger. Discovering predictable classifications. Technical Report CUCS62692, Dept. of Comp. Sci., University of Colorado at Boulder, November 1992.
[PMax] J. Schmidhuber and D. Prelinger. Discovering predictable classifications. Neural Computation, 5(4):625635, 1993. PDF.
[PO87] J. B. Pollack. On Connectionist Models of Natural Language Processing. PhD thesis, Computer Science Department, University of Illinois, Urbana, 1987.
[PO90] J. B. Pollack. Recursive Distributed Representations. Artificial Intelligence, 46(12):77105, 1990.
[PP] J. Schmidhuber. POWERPLAY: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem. Frontiers in Cognitive Science, 2013. ArXiv preprint (2011): arXiv:1112.5309 [cs.AI]
[PP1] R. K. Srivastava, B. Steunebrink, J. Schmidhuber. First Experiments with PowerPlay. Neural Networks, 2013. ArXiv preprint (2012): arXiv:1210.8385 [cs.AI].
[PP2] V. Kompella, M. Stollenga, M. Luciw, J. Schmidhuber. Continual curiositydriven skill acquisition from highdimensional video inputs for humanoid robots. Artificial Intelligence, 2015.
Relevant threads with many comments at reddit.com/r/MachineLearning, the largest machine learning forum with over 800k subscribers in 2019 (note that my name is often misspelled):
[R1] Reddit/ML, 2019. Hinton, LeCun, Bengio receive ACM Turing Award. This announcement contains more comments about Schmidhuber than about any of the awardees.
[R2] Reddit/ML, 2019. J. Schmidhuber really had GANs in 1990.
[R3] Reddit/ML, 2019. NeurIPS 2019 Bengio Schmidhuber MetaLearning Fiasco. Schmidhuber started metalearning (learning to learn—now a hot topic) in 1987^{[META1][META]} long before Bengio who suggested in public at N(eur)IPS 2019 that he did it before Schmidhuber.
[R4] Reddit/ML, 2019. Five major deep learning papers by G. Hinton did not cite similar earlier work by J. Schmidhuber.
[R5] Reddit/ML, 2019. The 1997 LSTM paper by Hochreiter & Schmidhuber has become the most cited deep learning research paper of the 20th century.
[R6] Reddit/ML, 2019. DanNet, the CUDA CNN of Dan Ciresan in J. Schmidhuber's team, won 4 image recognition challenges prior to AlexNet.
[R7] Reddit/ML, 2019. J. Schmidhuber on Seppo Linnainmaa, inventor of backpropagation in 1970.
[R8] Reddit/ML, 2019. J. Schmidhuber on Alexey Ivakhnenko, godfather of deep learning 1965.
[R9] Reddit/ML, 2019. We find it extremely unfair that Schmidhuber did not get the Turing award. That is why we dedicate this song to Juergen to cheer him up.
[R11] Reddit/ML, 2020. Schmidhuber: Critique of Honda Prize for Dr. Hinton
[R12] Reddit/ML, 2020. J. Schmidhuber: Critique of Turing Award for Drs. Bengio & Hinton & LeCun
[R58] Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65(6):386. This paper not only described single layer perceptrons, but also deeper multilayer perceptrons (MLPs). Although these MLPs did not yet have deep learning, because only the last layer learned,^{[DL1][DLH]} Rosenblatt basically had what much later was rebranded as Extreme Learning Machines (ELMs) without proper attribution.^{[ELM12][CONN21][T22]}
[R61] Joseph, R. D. (1961). Contributions to perceptron theory. PhD thesis, Cornell Univ.
[R62] Rosenblatt, F. (1962). Principles of Neurodynamics. Spartan, New York.
[RCNN] R. Girshick, J. Donahue, T. Darrell, J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. Preprint arXiv/1311.2524, Nov 2013.
[RCNN2] R. Girshick. Fast RCNN. Proc. of the IEEE international conference on computer vision, p. 14401448, 2015.
[RCNN3] K. He, G. Gkioxari, P. Dollar, R. Girshick. Mask RCNN. Preprint arXiv/1703.06870, 2017.
[RELU1] K. Fukushima (1969). Visual feature extraction by a multilayered network of analog threshold elements. IEEE Transactions on Systems Science and Cybernetics. 5 (4): 322333. doi:10.1109/TSSC.1969.300225. This work introduced rectified linear units or ReLUs.
[RELU2] C. v. d. Malsburg (1973). SelfOrganization of Orientation Sensitive Cells in the Striate Cortex. Kybernetik, 14:85100, 1973. See Table 1 for rectified linear units or ReLUs. Possibly this was also the first work on applying an EM algorithm to neural nets.
[RMSP] T. Tieleman, G. E. Hinton. Lecture 6.5rmsprop: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning 4.2 (2012): 2631.
[ROB] A. J. Robinson and F. Fallside. The utility driven dynamic error propagation network. Technical Report CUED/FINFENG/TR.1, Cambridge University Engineering Department, 1987.
[RPG] D. Wierstra, A. Foerster, J. Peters, J. Schmidhuber (2010). Recurrent policy gradients. Logic Journal of the IGPL, 18(5), 620634.
[RPG07] D. Wierstra, A. Foerster, J. Peters, J. Schmidhuber. Solving Deep Memory POMDPs with Recurrent Policy Gradients. Intl. Conf. on Artificial Neural Networks ICANN'07, 2007. PDF.
[RUM] DE Rumelhart, GE Hinton, RJ Williams (1985). Learning Internal Representations by Error Propagation. TR No. ICS8506, California Univ San Diego La Jolla Inst for Cognitive Science. Later version published as: Learning representations by backpropagating errors. Nature, 323, p. 533536 (1986). This experimental analysis of backpropagation did not cite the origin of the method,^{[BP15]} also known as the reverse mode of automatic differentiation. The paper also failed to cite the first working algorithms for deep learning of internal representations (Ivakhnenko & Lapa, 1965)^{[DEEP12][HIN]} as well as Amari's work (196768)^{[GD12]} on learning internal representations in deep nets through stochastic gradient descent. Even later surveys by the authors^{[DL3,3a]} failed to cite the prior art.^{[T22]}
[S93] D. Sherrington (1993). Neural networks: the spin glass approach. NorthHolland Mathematical Library, vol 51, 1993, p. 261291.
[S20] T. Sejnowski. The unreasonable effectiveness of deep learning in artificial intelligence. PNAS, January 28, 2020. Link. A misleading "history of deep learning" which goes more or less like this: "In 1969, Minsky & Papert^{[M69]} showed that shallow NNs without hidden layers are very limited and the field was abandoned until a new generation of neural network researchers took a fresh look at the problem in the 1980s."^{[S20]} However, the 1969 book^{[M69]} addressed a "problem" of Gauss & Legendre's shallow learning (~1800)^{[DL12][DLH]} that had already been solved 4 years prior by Ivakhnenko & Lapa's popular deep learning method,^{[DEEP12][DL2]} and then also by Amari's SGD for MLPs.^{[GD12]} Minsky was apparently unaware of this and failed to correct it later.^{[HIN](Sec. I)[T22](Sec. XIII)} Deep learning research was alive and kicking in the 1960s70s, especially outside of the Anglosphere.^{[DEEP12][GD13][CNN1][DL12][T22][DLH]}
[S80] B. Speelpenning (1980). Compiling Fast Partial Derivatives of Functions Given by Algorithms. PhD thesis, Department of Computer Science, University of Illinois, UrbanaChampaign.
[S2S] I. Sutskever, O. Vinyals, Quoc V. Le. Sequence to sequence learning with neural networks. In: Advances in Neural Information Processing Systems (NIPS), 2014, 31043112.
[S59] A. L. Samuel. Some studies in machine learning using the game of checkers. IBM Journal on Research and Development, 3:210229, 1959.
[STO51] H. Robbins, S. Monro (1951). A Stochastic Approximation Method. The Annals of Mathematical Statistics. 22(3):400, 1951.
[STO52] J. Kiefer, J. Wolfowitz (1952). Stochastic Estimation of the Maximum of a Regression Function. The Annals of Mathematical Statistics. 23(3):462, 1952.
[SA17] J. Schmidhuber. Falling Walls: The Past, Present and Future of Artificial Intelligence. Scientific American, Observations, Nov 2017.
[SCAN] J. Masci, A. Giusti, D. Ciresan, G. Fricout, J. Schmidhuber. A Fast Learning Algorithm for Image Segmentation with MaxPooling Convolutional Networks. ICIP 2013. Preprint arXiv:1302.1690.
[SE59] O. G. Selfridge (1959). Pandemonium: a paradigm for learning. In D. V. Blake and A. M. Uttley, editors, Proc. Symposium on Mechanisation of Thought Processes, p 511529, London, 1959.
[SNT] J. Schmidhuber, S. Heil (1996). Sequential neural text compression. IEEE Trans. Neural Networks, 1996. PDF. An earlier version appeared at NIPS 1995. Much later this was called a probabilistic language model.^{[T22]}
[SK75] D. Sherrington, S. Kirkpatrick (1975). Solvable Model of a SpinGlass. Phys. Rev. Lett. 35, 1792, 1975.
[ST] J. Masci, U. Meier, D. Ciresan, G. Fricout, J. Schmidhuber Steel Defect Classification with MaxPooling Convolutional Neural Networks. Proc. IJCNN 2012. PDF. Apparently, this was the first deep learning breakthrough in heavy industry.
[ST61] K. Steinbuch. Die Lernmatrix. (The learning matrix.) Kybernetik, 1(1):3645, 1961.
[ST95] W. Hilberg (1995). Karl Steinbuch, ein zu Unrecht vergessener Pionier der künstlichen neuronalen Systeme. (Karl Steinbuch, an unjustly forgotten pioneer of artificial neural systems.) Frequenz, 49(1995)12.
[SP93] A. Sperduti (1993). Encoding Labeled Graphs by Labeling RAAM. NIPS 1993: 11251132 One of the first papers on graph neural networks.
[SP94] A. Sperduti (1994). Labelling Recursive Autoassociative Memory. Connect. Sci. 6(4): 429459 (1994)
[SP95] A. Sperduti (1995). Stability properties of labeling recursive autoassociative memory. IEEE Trans. Neural Networks 6(6): 14521460 (1995)
[SPG95] A. Sperduti, A. Starita, C. Goller (1995). Learning Distributed Representations for the Classification of Terms. IJCAI 1995: 509517
[SPG96] A. Sperduti, D. Majidi, A. Starita (1996). Extended CascadeCorrelation for Syntactic and Structural Pattern Recognition. SSPR 1996: 9099
[SPG97] A. Sperduti, A. Starita (1997). Supervised neural networks for the classification of structures. IEEE Trans. Neural Networks 8(3): 714735, 1997.
[SV20] S. Vazire (2020). A toast to the error detectors. Let 2020 be the year in which we value those who ensure that science is selfcorrecting. Nature, vol 577, p 9, 2/2/2020.
[T19] ACM's justification of the 2018 A.M. Turing Award (announced in 2019). WWW link. Local copy 1 (HTML only). Local copy 2 (HTML only). [T22] debunks this justification.
[T20a] J. Schmidhuber (AI Blog, 25 June 2020). Critique of 2018 Turing Award for Drs. Bengio & Hinton & LeCun. A precursor of [T22].
[T21v1] J. Schmidhuber. Scientific Integrity, the 2021 Turing Lecture, and the 2018 Turing Award for Deep Learning. Technical Report IDSIA7721 (v1), IDSIA, 24 Sep 2021.
[T22] J. Schmidhuber (AI Blog, 2022). Scientific Integrity and the History of Deep Learning: The 2021 Turing Lecture, and the 2018 Turing Award. Technical Report IDSIA7721, IDSIA, Lugano, Switzerland, 2022. Debunking [T19] and [DL3a] .
[THE17] S. Baker (2017). Which countries and universities are leading on AI research? Times Higher Education World University Rankings, 2017. Link.
[TR1] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin (2017). Attention is all you need. NIPS 2017, pp. 59986008. This paper introduced the name "Transformers" for a now widely used NN type. It did not cite the 1991 publication on what's now called "unnormalized Transformers with linearized selfattention."^{[FWP06][TR57]} Schmidhuber also introduced the now popular attention terminology in 1993.^{[ATT][FWP2][R4]} See tweet of 2022 for 30year anniversary.
[TR2] J. Devlin, M. W. Chang, K. Lee, K. Toutanova (2018). Bert: Pretraining of deep bidirectional transformers for language understanding. Preprint arXiv:1810.04805.
[TR3] K. Tran, A. Bisazza, C. Monz. The Importance of Being Recurrent for Modeling Hierarchical Structure. EMNLP 2018, p 47314736. ArXiv preprint 1803.03585.
[TR4] M. Hahn. Theoretical Limitations of SelfAttention in Neural Sequence Models. Transactions of the Association for Computational Linguistics, Volume 8, p.156171, 2020.
[TR5] A. Katharopoulos, A. Vyas, N. Pappas, F. Fleuret. Transformers are RNNs: Fast autoregressive Transformers with linear attention. In Proc. Int. Conf. on Machine Learning (ICML), July 2020.
[TR6] K. Choromanski, V. Likhosherstov, D. Dohan, X. Song, A. Gane, T. Sarlos, P. Hawkins, J. Davis, A. Mohiuddin, L. Kaiser, et al. Rethinking attention with Performers. In Int. Conf. on Learning Representations (ICLR), 2021.
[TR7] H. Peng, N. Pappas, D. Yogatama, R. Schwartz, N. A Smith, L. Kong. Random feature attention. ICLR 2021.
[TUR] A. M. Turing. On computable numbers, with an application to the Entscheidungsproblem. Proceedings of the London Mathematical Society, Series 2, 41:230267. Received 28 May 1936. Errata appeared in Series 2, 43, pp 544546 (1937). 2nd explicit proof that the Entscheidungsproblem (decision problem) does not have a general solution.
[TUR1] A. M. Turing. Intelligent Machinery. Unpublished Technical Report, 1948. Link. In: Ince DC, editor. Collected works of AM Turing  Mechanical Intelligence. Elsevier Science Publishers, 1992.
[TUR2] A. M. Turing (1952). The Chemical Basis of Morphogenesis. Philosophical Transactions of the Royal Society of London 237 (641):3772.
[TUR21] J. Schmidhuber (AI Blog, Sep 2021). Turing Oversold. It's not Turing's fault, though.
[TUR3] G. Oppy, D. Dowe (2021). The Turing Test. Stanford Encyclopedia of Philosophy. Quote: "it is sometimes suggested that the Turing Test is prefigured in Descartes' Discourse on the Method. (Copeland (2000:527) finds an anticipation of the test in the 1668 writings of the Cartesian de Cordemoy. Abramson (2011a) presents archival evidence that Turing was aware of Descartes' language test at the time that he wrote his 1950 paper. Gunderson (1964) provides an early instance of those who find that Turing's work is foreshadowed in the work of Descartes.)"
[TUR3a] D. Abramson. Descartes' Influence on Turing. Studies in History and Philosophy of Science, 42:544551, 2011.
[TUR3b] Are computers conscious?—Panpsychism with Noam Chomsky  Theories of Everything. Mentioning the ancient "Turing Test" by Descartes. YouTube video, 2022.
[UN] J. Schmidhuber (AI Blog, 2021). 30year anniversary. 1991: First very deep learning with unsupervised or selfsupervised pretraining. Unsupervised hierarchical predictive coding (with selfsupervised target generation) finds compact internal representations of sequential data to facilitate downstream deep learning. The hierarchy can be distilled into a single deep neural network (suggesting a simple model of conscious and subconscious information processing). 1993: solving problems of depth >1000.
[UN0] J. Schmidhuber. Neural sequence chunkers. Technical Report FKI14891, Institut für Informatik, Technische Universität München, April 1991. PDF. Unsupervised/selfsupervised learning and predictive coding is used in a deep hierarchy of recurrent neural networks (RNNs) to find compact internal representations of long sequences of data, across multiple time scales and levels of abstraction. Each RNN tries to solve the pretext task of predicting its next input, sending only unexpected inputs to the next RNN above. The resulting compressed sequence representations greatly facilitate downstream supervised deep learning such as sequence classification. By 1993, the approach solved problems of depth 1000 [UN2] (requiring 1000 subsequent computational stages/layers—the more such stages, the deeper the learning). A variant collapses the hierarchy into a single deep net. It uses a socalled conscious chunker RNN which attends to unexpected events that surprise a lowerlevel socalled subconscious automatiser RNN. The chunker learns to understand the surprising events by predicting them. The automatiser uses a neural knowledge distillation procedure to compress and absorb the formerly conscious insights and behaviours of the chunker, thus making them subconscious. The systems of 1991 allowed for much deeper learning than previous methods. More.
[UN1] J. Schmidhuber. Learning complex, extended sequences using the principle of history compression. Neural Computation, 4(2):234242, 1992. Based on TR FKI14891, TUM, 1991.^{[UN0]} PDF. First working Deep Learner based on a deep RNN hierarchy (with different selforganising time scales), overcoming the vanishing gradient problem through unsupervised pretraining and predictive coding (with selfsupervised target generation). Also: compressing or distilling a teacher net (the chunker) into a student net (the automatizer) that does not forget its old skills—such approaches are now widely used. See also this tweet. More.
[UN2] J. Schmidhuber. Habilitation thesis, TUM, 1993. PDF. An ancient experiment on "Very Deep Learning" with credit assignment across 1200 time steps or virtual layers and unsupervised / selfsupervised pretraining for a stack of recurrent NN can be found here (depth > 1000).
[UN3] J. Schmidhuber, M. C. Mozer, and D. Prelinger. Continuous history compression. In H. Hüning, S. Neuhauser, M. Raus, and W. Ritschel, editors, Proc. of Intl. Workshop on Neural Networks, RWTH Aachen, pages 8795. Augustinus, 1993.
[UN4] G. E. Hinton, R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, Vol. 313. no. 5786, pp. 504—507, 2006. PDF. This work describes unsupervised pretraining of stacks of feedforward NNs (FNNs) called Deep Belief Networks (DBNs). It did not cite the much earlier 1991 unsupervised pretraining of stacks of more general recurrent NNs (RNNs)^{[UN03]} which introduced the first NNs shown to solve very deep problems. The 2006 justification of the authors was essentially the one Schmidhuber used for the 1991 RNN stack: each higher level tries to reduce the description length (or negative log probability) of the data representation in the level below.^{[HIN][T22][MIR]} This can greatly facilitate very deep downstream learning.^{[UN03]}
[UN5] Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle. Greedy layerwise training of deep networks. Proc. NIPS 06, pages 153160, Dec. 2006. The comment under reference^{[UN4]} applies here as well.
[URQ10] A. Urquhart. Von Neumann, Gödel and complexity theory. Bulletin of Symbolic Logic 16.4 (2010): 516530. Link.
[VAN1] S. Hochreiter. Untersuchungen zu dynamischen neuronalen Netzen. Diploma thesis, TUM, 1991 (advisor J. Schmidhuber). PDF. More on the Fundamental Deep Learning Problem.
[VAN2] Y. Bengio, P. Simard, P. Frasconi. Learning longterm dependencies with gradient descent is difficult. IEEE TNN 5(2), p 157166, 1994. Results are essentially identical to those of Schmidhuber's diploma student Hochreiter (1991).^{[VAN1]} Even after a common publication,^{[VAN3]} the first author of [VAN2] published papers^{[VAN45]} that cited only their own [VAN2] but not the original work.
[VAN3] S. Hochreiter, Y. Bengio, P. Frasconi, J. Schmidhuber. Gradient flow in recurrent nets: the difficulty of learning longterm dependencies. In S. C. Kremer and J. F. Kolen, eds., A Field Guide to Dynamical Recurrent Neural Networks. IEEE press, 2001. PDF.
[VAN4] Y. Bengio. Neural net language models. Scholarpedia, 3(1):3881, 2008. Link.
[VAN5] R. Pascanu, T. Mikolov, Y. Bengio. On the difficulty of training Recurrent Neural Networks. ICML 2013.
[VAR13] M. Y. Vardi (2013). Who begat computing? Communications of the ACM, Vol. 56(1):5, Jan 2013. Link.
[VID1] G. Hinton. The Next Generation of Neural Networks. Youtube video [see 28:16]. GoogleTechTalk, 2007. Quote: "Nobody in their right mind would ever suggest" to use plain backpropagation for training deep networks. However, in 2010, Schmidhuber's team in Switzerland showed^{[MLP12]} that unsupervised pretraining is not necessary to train deep NNs.
[VID2] Bloomberg Hello World. The Rise of AI. Youtube video, 2018. The narrator of this 2018 Bloomberg video is thanking Hinton for speech recognition and machine translation, although both were actually done (at production time of the video) on billions of smartphones by deep learning methods developed in Schmidhuber's labs in Germany and Switzerland (LSTM & CTC) long before Hinton's less successful methods.
[W45] G. H. Wannier (1945). The Statistical Problem in Cooperative Phenomena. Rev. Mod. Phys. 17, 50.
[WER87] P. J. Werbos. Building and understanding adaptive systems: A statistical/numerical approach to factory automation and brain research. IEEE Transactions on Systems, Man, and Cybernetics, 17, 1987.
[WER89] P. J. Werbos. Backpropagation and neurocontrol: A review and prospectus. In IEEE/INNS International Joint Conference on Neural Networks, Washington, D.C., volume 1, pages 209216, 1989.
[WI48] N. Wiener (1948). Time, communication, and the nervous system. Teleological mechanisms. Annals of the N.Y. Acad. Sci. 50 (4): 197219. Quote: "... the general idea of a computing machine is nothing but a mechanization of Leibniz's calculus ratiocinator."
[WID62] Widrow, B. and Hoff, M. (1962). Associative storage and retrieval of digital information in networks of adaptive neurons. Biological Prototypes and Synthetic Systems, 1:160, 1962.
[WU] Y. Wu et al. Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation. Preprint arXiv:1609.08144 (PDF), 2016. Based on LSTM which it mentions at least 50 times.
[XAV] X. Glorot, Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. Proc. 13th Intl. Conference on Artificial Intelligence and Statistics, PMLR 9:249256, 2010.
[YB20]
Y. Bengio. Notable Past Research.
WWW link (retrieved 15 May 2020).
Local copy (plain HTML only).
The author claims that in 1995
he "introduced the use of a hierarchy of time scales to combat the vanishing gradients issue"^{[HB96]}
although
Schmidhuber's publications on exactly this topic
date back to 199193.^{[UN02][UN]}
The author also writes that in
1999 he "introduced, for the first time, autoregressive neural networks for density estimation"
although Schmidhuber & Heil used a very similar setup for text compression
already in 1995.^{[SNT]}
.