My First Deep Learning System of 1991 + Deep Learning Timeline 19622013
Jürgen Schmidhuber
Pronounce: You_again Shmidhoobuh
Note: This draft is an experiment in rapid massive open online peer review.
Since 20 September 2013, it has absorbed many suggestions for improvements by experts.
(As a machine learning researcher I am obsessed with proper credit assignment.)
On 19 Dec 2013 a snapshot was stored as Technical Report
arXiv:1312.5548v1 [cs.NE].
Please send further corrections and comments to juergen@idsia.ch
Last update 30 December 2013
(compare G+ posts)
In 2009, our Deep Learning Artificial Neural Networks became the first Deep Learners to win official international pattern recognition competitions [A9] (with secret test set known only to the organisers); by 2012 they had won eight of them [A12], including the first contests on object detection in
large images [54] (at ICPR 2012) and image segmentation [53] (at ISBI 2012). In 2011, they achieved the
world's first superhuman visual pattern recognition results [A11]. Others implemented variants and have won additional contests since 2012, e.g., [A12,A13]. The field of Deep Learning research is far older though (see timeline further down).
My first Deep Learner dates back to 1991 [1,2]. It can perform credit assignment across hundreds of nonlinear operators or neural layers, by using unsupervised pretraining for a stack of recurrent neural networks (RNN) (deep by nature) as in the figure above. (Such RNN are general computers more powerful than normal feedforward NN, and can encode entire sequences of inputs.)
The basic idea is still relevant today. Each RNN is trained for a while in unsupervised fashion to predict its next input. From then on, only unexpected inputs (errors) convey new information and get fed to the next higher RNN which thus ticks on a slower, selforganising time scale. It can easily be shown that no information gets lost. It just gets compressed (note that much of machine learning is essentially about compression). We get less and less redundant input sequence encodings in deeper and deeper levels of this hierarchical temporal memory, which compresses data in both space (like feedforward NN) and time. There also is a continuous variant [47].
One ancient illustrative Deep Learning experiment of 1993 [2] required credit assignment across 1200 time steps, or through 1200 subsequent nonlinear virtual layers. The top level code of the initially unsupervised RNN stack, however, got so compact that (previously infeasible) sequence classification through additional supervised learning became possible.
There is a way of compressing higher levels down into lower levels, thus partially collapsing the hierarchical temporal memory. The trick is to retrain lowerlevel RNN to continually imitate (predict) the hidden units of already trained, slower, higherlevel RNN, through additional predictive output neurons [1,2]. This helps the lower RNN to develop appropriate, rarely changing memories that may bridge very long time lags.
The Deep Learner of 1991 was a first way of overcoming the
Fundamental Deep Learning Problem
identified and analysed in 1991 by my very first student (now professor) Sepp Hochreiter: the problem of vanishing or exploding gradients [3,4,4a,A5]. The latter motivated all our subsequent Deep Learning research of the 1990s and 2000s.
Through supervised
LSTM RNN (1997)
(e.g., [5,6,7,A7]) we could eventually perform similar feats as with the 1991 system [1,2], overcoming the
Fundamental Deep Learning Problem without any unsupervised pretraining. Moreover, LSTM could also learn tasks unlearnable by the partially unsupervised 1991 chunker [1,2].
Particularly successful are stacks of LSTM RNN [10] trained by
Connectionist Temporal Classification (CTC) [8]. On faster computers of 2009, this became the first RNN system ever to win an official international pattern recognition competition [A9], through the work of my PhD student and postdoc Alex Graves, e.g., [10]. To my knowledge, this also was the first Deep Learning system ever (recurrent or not) to win such a contest. (In fact, it won three different ICDAR 2009 contests on connected handwriting in three different languages, e.g., [11,A9,A13].) A while ago, Alex moved on to Geoffrey Hinton's lab (Univ. Toronto), where a stack [10] of our bidirectional LSTM RNN [7] also broke a famous TIMIT speech recognition record [12,A13], despite thousands of man years previously spent on HMMbased speech recognition research.
CTCLSTM also helped to score first at NIST's OpenHaRT2013 evaluation [12a].
Recently, wellknown entrepreneurs also got interested in such hierarchical temporal memories [13,14].
The expression Deep Learning actually got coined relatively late, around 2006, in the context of unsupervised pretraining for less general feedforward networks [15,A8]. Such a system reached 1.2% error rate [15] on the MNIST handwritten digits [16], perhaps the most famous benchmark of Machine Learning. Our team first showed that good old backpropagation [A1] on GPUs (with training pattern distortions [42,43] but without any unsupervised pretraining) can actually achieve a three times better result of 0.35% [17,A10]  back then, a world record (a previous standard net achieved 0.7% [43]; a backproptrained [16] Convolutional NN (CNN) [19a,19,16,16a] got 0.39% [49,A8]; plain backprop without distortions except for small saccadic eye movementlike translations already got 0.95%). Then we replaced our standard net by a biologically rather plausible architecture inspired by early neurosciencerelated work [19a,18,19,16]: Deep and Wide GPUbased MultiColumn MaxPooling CNN (MCMPCNN) [21,22,A11] with alternating backpropbased [16,16a,50] weightsharing convolutional layers [19,16,23] and winnertakeall [19a,19] maxpooling [20,24,50,46] layers (see [55] for early GPUbased CNN). MCMPCNN are committees of MPCNN [25a] with simple democratic output averaging (compare earlier more sophisticated ensemble methods [48]). Object detection [54,54c,54a,A12] and image segmentation [53,A12] profit from fast MPCNNbased image scans [28,28a]. Our supervised GPUMCMPCNN was the first method to achieve superhuman performance in an official international competition (with secret test set known only to the organisers) [25,25ac,A11] (compare [51]), and the first with humancompetitive performance (around 0.2%) on MNIST [22]. Since 2011, it has won numerous additional competitions on a routine basis [A11A13].
Our GPUMPCNN [21,A11] were adopted by the groups of Univ. Toronto/Stanford/Google, e.g., [26,27,A12,A13].
Apple Inc., the most profitable smartphone maker, hired Ueli Meier, member of our Deep Learning team that won the ICDAR 2011 Chinese handwriting contest [11,22].
ArcelorMittal, the world's top steel producer, is using our methods for material defect detection, e.g., [28]. Other users include
a leading automotive supplier, recent startups such as deepmind (which hired four of my former PhD students/postdocs), and many other companies and leading research labs. One of the most important applications of our techniques is biomedical imaging [54], e.g., for cancer prognosis or plaque detection in CT heart scans.
Remarkably, the most successful Deep Learning algorithms in most international contests since 2009 [A9A13] are adaptations and extensions of an over 40yearold algorithm, namely, supervised efficient backprop [A1,60,29a] (compare [30,31,58,59,61]) or BPTT/RTRL for RNN, e.g., [3234,3739]. (Exceptions include two 2011 contests specialised on
transfer learning [44]  but compare [45]). In particular, as of 2013, stateoftheart feedforward nets [A11A13] are GPUbased [21] multicolumn [22] combinations of two ancient concepts: Backpropagation [A1] applied [16a] to Neocognitronlike convolutional architectures [A2] (with maxpooling layers [20,50,46] instead of alternative [19a,19,40,20a] local winnertakeall methods). (Plus additional tricks from the 1990s and 2000s, e.g., [41a,41b,41c].) In the quite different deep recurrent case, supervised systems also dominate, e.g., [5,8,10,9,39,12,A9,A13].
In particular, most competitionwinning or benchmark recordsetting Deep Learners [A9A13] now use one of two supervised techniques developed in my lab: (1) recurrent LSTM (1997) [A7] trained by CTC (2006) [8], or (2) feedforward GPUMPCNN (2011) [21,A11] (building on earlier work since the 1960s mentioned in the text above).
Nevertheless, in many applications it can still be advantageous to combine the best of both worlds  supervised learning and unsupervised pretraining, like in my 1991 system described above [1,2,A6].
Acknowledgments: Thanks for valuable comments to Geoffrey Hinton, Kunihiko Fukushima, Yoshua Bengio, Sven Behnke, Yann LeCun, Sepp Hochreiter, Mike Mozer, Marc'Aurelio Ranzato, Andreas Griewank, Paul Werbos, Shunichi Amari, Seppo Linnainmaa, Peter Norvig, YuChi Ho, Alex Graves, Dan Ciresan, Jonathan Masci, Stuart Dreyfus, and others. Graphics: Fibonacci Web Design
Timeline of Deep Learning Highlights
(compare references below)
[A0] 1962: Neurobiological Inspiration Through Simple Cells and Complex Cells
Hubel and Wiesel described simple cells and complex cells in the visual cortex [18], inspiration for later deep artificial neural network (NN) architectures [A2] used in certain modern awardwinning Deep Learners [A11A12]
(I was conceived in 1962)
[A1] 1970 ± a Decade or so: Backpropagation
Error functions and their gradients for complex, nonlinear, multistage, differentiable, NNrelated systems have been discussed at least
since the early 1960s, e.g., [5658,6466]. Gradient descent [70] in such systems can be performed
[57a,57,58] by iterating the ancient chain rule [68,69] in dynamic
programming style [67] (compare simplified derivation using chain rule only [57b]).
However, efficient error backpropagation (BP) in arbitrary, possibly sparse, NNlike networks
apparently was first described by Linnainmaa
in 1970 [6061]. This is also known as the reverse mode of automatic differentiation [56],
where the costs of forward activation spreading
essentially equal the costs of backward derivative calculation.
See early FORTRAN code [60]. Compare [62,29c] and some
NNrelated discussion [29] (section 5.5.1),
and the first NNspecific efficient BP of 1981 by Werbos [29a,29b].
Compare [30,31,59]
and generalisations for sequenceprocessing recurrent NN, e.g., [3234,3739].
See also natural gradients [63].
As of 2013, BP is still the central Deep Learning algorithm.
[A2] 1979: Deep Neocognitron, Weight Sharing, Convolution
Fukushima's
Deep Neocognitron Architecture [19a,19,40] incorporated neurophysiological insights [A0,18] and introduced weightsharing convolutional neural layers as well as winnertakeall layers. It is very similar to the architecture of modern, feedforward, competitionwinning, purely supervised, gradientbased Deep Learners [A11A12] (but uses local unsupervised learning rules instead).
[A3] 1987: Autoencoder Hierarchies
Ideas published by Ballard on unsupervised autoencoder hierarchies [35], related to post2000 feedforward Deep Learners based on unsupervised pretraining, e.g., [15,A8]; compare survey [36] and somewhat related RAAMs [52]
[A4] 1989: Backpropagation for CNN
Backprop [A1] applied by LeCun et al. [16,16a] to Fukushima's weightsharing convolutional neural layers [A2,19a,19,16]  this combination has become an essential ingredient of many modern, feedforward, competitionwinning, visual Deep Learners [A11A12]
[A5] 1991: Fundamental Deep Learning Problem
By the early 1990s, experiments had shown that deep feedforward or recurrent networks are hard to
train by backpropagation [A1]. My student
Hochreiter discovered and analyzed the reason, namely, the
Fundamental Deep Learning Problem
due to vanishing or exploding gradients [3]. Compare [4]
[A6] 1991: Deep Hierarchy of Recurrent NN
My first recurrent Deep Learning system (present page) [1,2]
partially overcame the fundamental problem [A5]
through a deep RNN stack pretrained in unsupervised fashion
[1,2]
to accelerate subsequent supervised learning.
This was a working Deep Learner in the
modern post2000 sense, and also the first Neural Hierarchical Temporal Memory.
[A7] 1997: Supervised Deep Learner (LSTM)
Long ShortTerm Memory (LSTM) RNN
became the
first purely supervised Deep Learner,
e.g., [510,12,A9]. LSTM RNN were able to learn solutions to many previously unlearnable problems.
[A8] 2006: Deep Belief Networks / CNN Results
A paper by Hinton and Salakhutdinov [15] focused on unsupervised pretraining of feedforward NN
to accelerate subsequent supervised learning (compare [A6]). This
helped to arouse interest in deep NN (keywords: restricted Boltzmann machines, Deep Belief Networks).
In the same year, a supervised BPtrained [A1,A4] CNN [A2,A4] by Ranzato et al. set a new record [49] on the famous MNIST handwritten digit recognition benchmark [16], using training pattern
deformations [42,43].
[A9] 2009: First Competitions Won by Deep Learning
First official international pattern recognition contests (with secret test sets) won by Deep Learning: Several connected handwriting competitions at ICDAR 2009 were won by LSTM RNN [A7] performing simultaneous segmentation and recognition [10,11].
[A10] 2010: Plain Backpropagation on GPUs Yields Excellent Results
New MNIST record [17] set
by good old backpropagation [A1] in deep but otherwise
standard NN (no unsupervised pretraining, no convolution, but training pattern
deformations), through a fast GPU implementation [17].
(A year later, the first humancompetitive performance on MNIST
was achieved by a deep MCMPCNN [22,A11].)
[A11] 2011: MPCNN on GPU  First Superhuman Visual Pattern Recognition
Ciresan et al. introduced
supervised GPUbased MaxPooling CNN or convnets (GPUMPCNN) [21],
today used by most if not all feedforward competitionwinning deep NN [A12A13]. The
first superhuman visual pattern recognition (on a secret test set) was achieved [25,25ac] (twice better than humans, three times better than the closest artificial NN competitor, six times better than the best nonneural method),
through deep and wide MultiColumn (MC) [25a,48] GPUMPCNN [21], the current gold standard for deep feedforward NN, now used in many applications [A12A13].
[A12] 2012: First Contests Won on Object Detection and Image Segmentation
An imagescanning [28,28a] GPUMPCNN [21,A11] became the
first Deep Learner to win a
contest on visual object detection in large images [54,54c,54d,54a]
(as opposed to mere recognition/classification): the ICPR 2012 contest on mitosis detection.
New record [26] set on the ImageNet classification benchmark with the help of
an MC [A11] GPUMPCNN variant.
First pure image segmentation contest (ISBI 2012) won by a Deep Learner
(again an imagescanning GPUMPCNN) [53,53a,53b]  the 8th international pattern recognition contest won by my team since 2009 (interview).
[A13] 2013: More Contests and Benchmark Records
New TIMIT phoneme recognition record
set by deep LSTM RNN [12].
New record (almost human performance) [45a] on the ICDAR Chinese handwriting recognition
benchmark (over 3700 classes) set on a desktop machine
by a deep GPUMCMPCNN.
MICCAI 2013 Grand Challenge on Mitosis Detection
won by a GPUMPCNN [5454b].
GPUMPCNN [21] also help to achieve new best results
on ImageNet classification [26a] and PASCAL object detection [54e].
Additional contests mentioned in the web pages of
J.S. at
the Swiss AI Lab IDSIA and
G.H. at
the University of Toronto.
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[53b]
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Deep Learning NN win MICCAI 2013 Grand Challenge and 2012 ICPR Contest on Mitosis Detection
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PDF.
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[54c]
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