Critique of Paper by "Deep Learning Conspiracy" (Nature 521 p 436)
Pronounce: You_again Shmidhoobuh
Machine learning is the science of credit assignment. The machine learning community itself profits from proper credit assignment to its members. 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). Relatively young research areas such as machine learning should adopt the honor code of mature fields such as mathematics: if you have a new theorem, but use a proof technique similar to somebody else's, you must make this very clear. If you "re-invent" something that was already known, and only later become aware of this, you must at least make it clear later.
As a case in point, let me now comment on a recent
article in Nature (2015)
about "deep learning" in artificial neural networks (NNs), by LeCun & Bengio & Hinton (LBH for short), three CIFAR-funded collaborators who call themselves the "deep learning conspiracy" (e.g., LeCun, 2015). They heavily cite each other. Unfortunately, however, they fail to credit the pioneers of the field, which originated half a century ago.
All references below are taken from the recent deep learning overview (Schmidhuber, 2015), except for a few papers listed beneath this critique focusing on nine items.
LBH's survey does not even mention the father of deep learning, Alexey Grigorevich Ivakhnenko, who published the first general, working learning algorithms for deep networks (e.g., Ivakhnenko and Lapa, 1965). A paper from 1971 already described a deep learning net with 8 layers (Ivakhnenko, 1971), trained by a highly cited method still popular in the new millennium. Given a training set of input vectors with corresponding target output vectors, layers of additive and multiplicative neuron-like nodes are incrementally grown and trained by regression analysis, then pruned with the help of a separate validation set, where regularisation is used to weed out superfluous nodes. The numbers of layers and nodes per layer can be learned in problem-dependent fashion.
LBH discuss the importance
and problems of gradient descent-based learning
backpropagation (BP), and cite their own papers on BP, plus a few others, but fail to mention BP's inventors. BP's continuous form was derived in the early 1960s (Bryson, 1961; Kelley, 1960; Bryson and Ho, 1969). Dreyfus (1962) published the elegant derivation of BP based on the chain rule only. BP's modern efficient version for discrete sparse networks (including FORTRAN code) was published by Linnainmaa (1970). Dreyfus (1973) used BP to change weights of controllers in proportion to such gradients. By 1980, automatic differentiation could derive BP for any differentiable graph (Speelpenning, 1980). Werbos (1982) published the first application of BP to NNs, extending thoughts in his 1974 thesis (cited by LBH), which did not have Linnainmaa's (1970) modern, efficient form of BP. BP for NNs on computers 10,000 times faster per Dollar than those of the 1960s can yield useful internal representations, as shown by Rumelhart et al. (1986), who also did not cite
LBH claim: "Interest in deep feedforward networks [FNNs] was revived around 2006 (refs 31-34) by a group of researchers brought together by the Canadian Institute for Advanced Research (CIFAR)." Here they refer exclusively to their own labs, which is misleading. For example, by 2006, many researchers had used deep nets of the Ivakhnenko type for decades. LBH also ignore earlier, closely related work funded by other sources, such as the deep hierarchical convolutional neural abstraction pyramid (e.g., Behnke, 2003b), which was trained to reconstruct images corrupted by structured noise, enforcing increasingly abstract image representations in deeper and deeper layers.
(BTW, the term "Deep Learning" (the very title of LBH's paper)
was introduced to Machine Learning by Dechter (1986), and to NNs by Aizenberg et al (2000),
none of them cited by LBH.)
LBH point to their own work (since 2006) on unsupervised pre-training of deep FNNs prior to BP-based fine-tuning, but fail to clarify that this was very similar in spirit and justification to the much earlier successful work on unsupervised pre-training of deep recurrent NNs (RNNs) called
neural history compressors
(Schmidhuber, 1992b, 1993b). Such RNNs are even more general than FNNs. A first RNN uses unsupervised learning to predict its next input. Each higher level RNN tries to learn a compressed representation of the information in the RNN below, to minimise the description length (or negative log probability) of the data. The top RNN may then find it easy to classify the data by supervised learning. One can even "distill" a higher, slow RNN (the teacher) into a lower, fast RNN (the student), by forcing the latter to predict the hidden units of the former. Such systems could solve previously unsolvable very deep learning tasks, and started our
long series of
successful deep learning methods since the early 1990s (funded by Swiss SNF, German DFG, EU and others),
long before 2006, although everybody had to wait for faster computers to make very deep learning
LBH also ignore earlier FNNs that profit from unsupervised pre-training prior to BP-based fine-tuning (e.g., Maclin and Shavlik, 1995). They cite Bengio et al.'s post-2006 papers on unsupervised stacks of autoencoders, but omit the original work on this (Ballard, 1987).
LBH write that "unsupervised learning (refs 91-98) had a catalytic effect in reviving interest in deep learning, but has since been overshadowed by the successes of purely supervised learning." Again they almost exclusively cite post-2005 papers co-authored by themselves. By 2005, however, this transition from unsupervised to supervised learning was an old hat, because back in the 1990s, our
unsupervised RNN-based history compressors (see above) were largely phased out by our purely supervised
Long Short-Term Memory (LSTM) RNNs,
now widely used in industry and academia for processing sequences such as speech and video. Around 2010, history repeated itself, as unsupervised FNNs were largely replaced by purely supervised FNNs, after our plain GPU-based deep FNN (Ciresan et al., 2010) trained by BP with pattern distortions (Baird, 1990) set a new record on the famous MNIST handwritten digit dataset, suggesting that advances in exploiting modern computing hardware were more important than advances in algorithms.
While LBH mention the significance of fast GPU-based NN implementations, they fail to cite the
originators of this approach (Oh and Jung, 2004).
In the context of convolutional neural networks (ConvNets), LBH mention pooling, but not its pioneer (Weng, 1992), who replaced Fukushima's (1979) spatial averaging by max-pooling, today widely used by many, including LBH, who
write: "ConvNets were largely forsaken by the mainstream computer-vision and machine-learning communities until the ImageNet competition in 2012," citing Hinton's 2012 paper (Krizhevsky et al., 2012). This is misleading. Earlier, committees of max-pooling ConvNets were accelerated on GPU (Ciresan et al., 2011a), and used to achieve the
first superhuman visual pattern recognition
in a controlled machine learning competition, namely, the highly visible IJCNN 2011 traffic sign recognition contest in Silicon Valley (relevant for self-driving cars). The system was twice better than humans, and three times better than the nearest non-human competitor (co-authored by LeCun of LBH). It also broke several other machine learning records, and surely was not "forsaken" by the machine-learning community. In fact, the later system (Krizhevsky et al. 2012) was very similar to the earlier 2011 system. Here one must also mention that the first official international contests won with the help of ConvNets actually date back to 2009 (three TRECVID competitions) - compare Ji et al. (2013). A GPU-based max-pooling ConvNet committee also was the
first deep learner to win a contest on visual object discovery in large images, namely, the ICPR 2012 Contest on Mitosis Detection in Breast Cancer Histological Images (Ciresan et al., 2013). A similar system was
the first deep learning FNN to win a pure image segmentation contest
(Ciresan et al., 2012a), namely, the ISBI 2012 Segmentation of Neuronal Structures in EM Stacks Challenge.
LBH discuss their FNN-based speech recognition successes in 2009 and 2012, but fail to mention that deep LSTM RNNs had outperformed traditional speech recognizers on certain tasks already in 2007 (Fernández et al., 2007)
(and traditional connected handwriting recognisers by 2009),
and that today's speech recognition conferences are dominated by (LSTM) RNNs, not by FNNs of 2009 etc.
While LBH cite work co-authored by Hinton on LSTM RNNs with several LSTM layers, this approach was pioneered much earlier (e.g., Fernandez et al., 2007).
LBH mention recent proposals such as "memory networks" and the somewhat misnamed "Neural Turing Machines"
(which do not have an unlimited number of memory cells like real Turing machines), but ignore
very similar proposals of the early 1990s,
on neural stack machines, fast weight networks, self-referential
RNNs that can address and rapidly modify their own weights during runtime, etc (e.g., AMAmemory 2015).
They write that "Neural Turing machines can be taught algorithms," as if this was something new, although LSTM RNNs were taught algorithms many years earlier,
even entire learning algorithms
(e.g., Hochreiter et al., 2001b).
In their outlook,
LBH mention "RNNs that use reinforcement learning to decide where to look" but not that
they were introduced a quarter-century ago
(Schmidhuber & Huber, 1991). Compare the more recent Compressed NN Search for large attention-directing RNNs
(Koutnik et al., 2013).
One more little quibble: While LBH suggest that "the earliest days of pattern recognition" date back to the 1950s, the cited methods are actually very similar to linear regressors of the early 1800s, by
and Legendre. Gauss famously used such techniques to recognize predictive patterns in observations of the asteroid Ceres.
LBH may be backed by the best PR machines of the Western world (Google hired Hinton; Facebook hired LeCun). In the long run, however, historic scientific facts
(as evident from the published record) will be stronger than any PR.
There is a long tradition of insights into deep learning,
and the community as a whole will benefit from
appreciating the historical foundations.
The contents of this critique may be used (also verbatim) for educational and non-commercial purposes, including articles for Wikipedia and similar sites. Compare a popular G+ version with comments, as well as additional G+ posts on deep learning.
Y. LeCun, Y. Bengio, G. Hinton (2015). Deep Learning. Nature 521, 436-444.
Y. LeCun (2015). IEEE Spectrum Interview by L. Gomes, Feb 2015.
R. Dechter (1986). Learning while searching in constraint-satisfaction problems. University of California, Computer Science Department, Cognitive Systems Laboratory. First paper to introduce the term "Deep Learning" to Machine Learning.
I. Aizenberg, N.N. Aizenberg, and J. P.L. Vandewalle (2000). Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications. Springer Science & Business Media. First paper to introduce the term "Deep Learning" to Neural Networks. Compare a popular G+ post on this.
J. Schmidhuber (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85-117.
AMAmemory (2015): Answer at reddit AMA (Ask Me Anything) on "memory networks" etc (with references):