First Deep Learner 1991 - credits: background  partially reusing a neuron image of BrainMaps.org / UC Davis

My First Deep Learning System of 1991
+ Deep Learning Timeline 1962-2013

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 pre-training 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, self-organising 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 lower-level RNN to continually imitate (predict) the hidden units of already trained, slower, higher-level 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 pre-training. 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 HMM-based speech recognition research. CTC-LSTM also helped to score first at NIST's OpenHaRT2013 evaluation [12a].

Recently, well-known 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 pre-training 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 pre-training) 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 backprop-trained [16] Convolutional NN (CNN) [19a,19,16,16a] got 0.39% [49,A8]; plain backprop without distortions except for small saccadic eye movement-like translations already got 0.95%). Then we replaced our standard net by a biologically rather plausible architecture inspired by early neuroscience-related work [19a,18,19,16]: Deep and Wide GPU-based Multi-Column Max-Pooling CNN (MCMPCNN) [21,22,A11] with alternating backprop-based [16,16a,50] weight-sharing convolutional layers [19,16,23] and winner-take-all [19a,19] max-pooling [20,24,50,46] layers (see [55] for early GPU-based 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 MPCNN-based image scans [28,28a]. Our supervised GPU-MCMPCNN was the first method to achieve superhuman performance in an official international competition (with secret test set known only to the organisers) [25,25a-c,A11] (compare [51]), and the first with human-competitive performance (around 0.2%) on MNIST [22]. Since 2011, it has won numerous additional competitions on a routine basis [A11-A13].

Our GPU-MPCNN [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 start-ups 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 [A9-A13] are adaptations and extensions of an over 40-year-old algorithm, namely, supervised efficient backprop [A1,60,29a] (compare [30,31,58,59,61]) or BPTT/RTRL for RNN, e.g., [32-34,37-39]. (Exceptions include two 2011 contests specialised on transfer learning [44] - but compare [45]). In particular, as of 2013, state-of-the-art feedforward nets [A11-A13] are GPU-based [21] multi-column [22] combinations of two ancient concepts: Backpropagation [A1] applied [16a] to Neocognitron-like convolutional architectures [A2] (with max-pooling layers [20,50,46] instead of alternative [19a,19,40,20a] local winner-take-all 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 competition-winning or benchmark record-setting Deep Learners [A9-A13] now use one of two supervised techniques developed in my lab: (1) recurrent LSTM (1997) [A7] trained by CTC (2006) [8], or (2) feedforward GPU-MPCNN (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 pre-training, 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, Shun-ichi Amari, Seppo Linnainmaa, Peter Norvig, Yu-Chi 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 award-winning Deep Learners [A11-A12] (I was conceived in 1962)

[A1] 1970 ± a Decade or so: Backpropagation
Error functions and their gradients for complex, nonlinear, multi-stage, differentiable, NN-related systems have been discussed at least since the early 1960s, e.g., [56-58,64-66]. 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, NN-like networks apparently was first described by Linnainmaa in 1970 [60-61]. 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 NN-related discussion [29] (section 5.5.1), and the first NN-specific efficient BP of 1981 by Werbos [29a,29b]. Compare [30,31,59] and generalisations for sequence-processing recurrent NN, e.g., [32-34,37-39]. 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 weight-sharing convolutional neural layers as well as winner-take-all layers. It is very similar to the architecture of modern, feedforward, competition-winning, purely supervised, gradient-based Deep Learners [A11-A12] (but uses local unsupervised learning rules instead).

[A3] 1987: Autoencoder Hierarchies
Ideas published by Ballard on unsupervised autoencoder hierarchies [35], related to post-2000 feedforward Deep Learners based on unsupervised pre-training, 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 weight-sharing convolutional neural layers [A2,19a,19,16] - this combination has become an essential ingredient of many modern, feedforward, competition-winning, visual Deep Learners [A11-A12]

[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 pre-trained in unsupervised fashion [1,2] to accelerate subsequent supervised learning. This was a working Deep Learner in the modern post-2000 sense, and also the first Neural Hierarchical Temporal Memory.

[A7] 1997: Supervised Deep Learner (LSTM)
Long Short-Term Memory (LSTM) RNN became the first purely supervised Deep Learner, e.g., [5-10,12,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 pre-training 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 BP-trained [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 pre-training, no convolution, but training pattern deformations), through a fast GPU implementation [17]. (A year later, the first human-competitive 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 GPU-based Max-Pooling CNN or convnets (GPU-MPCNN) [21], today used by most if not all feedforward competition-winning deep NN [A12-A13]. The first superhuman visual pattern recognition (on a secret test set) was achieved [25,25a-c] (twice better than humans, three times better than the closest artificial NN competitor, six times better than the best non-neural method), through deep and wide Multi-Column (MC) [25a,48] GPU-MPCNN [21], the current gold standard for deep feedforward NN, now used in many applications [A12-A13].

[A12] 2012: First Contests Won on Object Detection and Image Segmentation
An image-scanning [28,28a] GPU-MPCNN [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] GPU-MPCNN variant. First pure image segmentation contest (ISBI 2012) won by a Deep Learner (again an image-scanning GPU-MPCNN) [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 GPU-MCMPCNN. MICCAI 2013 Grand Challenge on Mitosis Detection won by a GPU-MPCNN [54-54b]. GPU-MPCNN [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.



References (96)

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[25c] Qualifying for IJCNN 2011 competition: results of 1st stage (January 2011)

[25d] Results for IJCNN 2011 competition (2 August 2011)

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[53b] Segmentation of neuronal structures in EM stacks challenge - IEEE International Symposium on Biomedical Imaging (ISBI) 2012

[54] Deep Learning NN win MICCAI 2013 Grand Challenge and 2012 ICPR Contest on Mitosis Detection

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[54b] 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

[54c] ICPR 2012 Contest on Mitosis Detection in Breast Cancer Histological Images (MITOS dataset). Organizers: IPAL Laboratory, TRIBVN Company, Pitie-Salpetriere Hospital, CIALAB of Ohio State Univ.

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