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

12/19/2013 ∙ by Jürgen Schmidhuber, et al. ∙ 0

Deep Learning has attracted significant attention in recent years. Here I present a brief overview of my first Deep Learner of 1991, and its historic context, with a timeline of Deep Learning highlights.



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1 Timeline of Deep Learning Highlights

1.1 1962: Neurobiological Inspiration Through Simple Cells and Complex Cells

Hubel and Wiesel described simple cells and complex cells in the visual cortex [49]. This inspired later deep artificial neural network (NN) architectures (TL 1.3) used in certain modern award-winning Deep Learners (TL 1.12-1.14). (The author of the present paper was conceived in 1962.)

1.2 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., [41, 50, 13, 27, 12, 93, 4, 26]. Gradient descent [42] in such systems can be performed [13, 50, 12]

by iterating the ancient chain rule 

[57, 58] in dynamic programming style [9] (compare simplified derivation using chain rule only [27]). However, efficient error backpropagation (BP) in arbitrary, possibly sparse, NN-like networks apparently was first described by Linnainmaa in 1970 [59, 60] (he did not refer to NN though). BP is also known as the reverse mode of automatic differentiation [41], where the costs of forward activation spreading essentially equal the costs of backward derivative calculation. See early FORTRAN code [59], and compare [66]. Compare the concept of ordered derivative [89] and related work [28], with NN-specific discussion [89] (section 5.5.1), and the first NN-specific efficient BP of 1981 by Werbos [90, 92]. Compare [53, 75, 67] and generalisations for sequence-processing recurrent NN, e.g., [94, 73, 91, 68, 80, 61]. See also natural gradients [5]. As of 2013, BP is still the central Deep Learning algorithm.

1.3 1979: Deep Neocognitron, Weight Sharing, Convolution

Fukushima’s deep Neocognitron architecture [29, 30, 31] incorporated neurophysiological insights (TL 1.1)  [49]. It introduced weight-sharing Convolutional Neural Networks (CNN) as well as winner-take-all layers. It is very similar to the architecture of modern, competition-winning, purely supervised, feedforward, gradient-based Deep Learners (TL 1.12-1.14). Fukushima, however, used local unsupervised learning rules instead.

1.4 1987: Autoencoder Hierarchies

In 1987, Ballard published ideas on unsupervised autoencoder hierarchies 

[7], related to post-2000 feedforward Deep Learners (TL 1.9) based on unsupervised pre-training, e.g., [44]; compare survey [45] and somewhat related RAAMs [69].

1.5 1989: Backpropagation for CNN

LeCun et al. [54, 55] applied backpropagation (TL 1.2) to Fukushima’s weight-sharing convolutional neural layers (TL 1.3[29, 30, 54]. This combination has become an essential ingredient of many modern, competition-winning, feedforward, visual Deep Learners (TL 1.12-1.13).

1.6 1991: Fundamental Deep Learning Problem

By the early 1990s, experiments had shown that deep feedforward or recurrent networks are hard to train by backpropagation (TL 1.2). My student Hochreiter [46] discovered and analyzed the reason, namely, the Fundamental Deep Learning Problem due to vanishing or exploding gradients. Compare [47].

1.7 1991: Deep Hierarchy of Recurrent NN

My first recurrent Deep Learning system (present paper) partially overcame the fundamental problem (TL 1.6) through a deep RNN stack pre-trained in unsupervised fashion [79, 81, 82] 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.

1.8 1997: Supervised Deep Learner (LSTM)

Long Short-Term Memory (LSTM) recurrent neural networks (RNN) became the first purely supervised Deep Learners, e.g., [48, 33, 39, 36, 37, 40, 38]. LSTM RNN were able to learn solutions to many previously unlearnable problems (see also TL 1.10, TL 1.14).

1.9 2006: Deep Belief Networks / CNN Results

A paper by Hinton and Salakhutdinov [44] focused on unsupervised pre-training of feedforward NN to accelerate subsequent supervised learning (compare TL 1.7

). This helped to arouse interest in deep NN (keywords: restricted Boltzmann machines; Deep Belief Networks). In the same year, a BP-trained CNN (TL

1.3, TL 1.5) by Ranzato et al. [70] set a new record on the famous MNIST handwritten digit recognition benchmark [54], using training pattern deformations [6, 86].

1.10 2009: First Competitions Won by Deep Learning

2009 saw the first Deep Learning systems to win official international pattern recognition contests (with secret test sets known only to the organisers): three connected handwriting competitions at ICDAR 2009 were won by deep LSTM RNN [40, 83] performing simultaneous segmentation and recognition.

1.11 2010: Plain Backpropagation on GPUs Yields Excellent Results

In 2010, a new MNIST record was set by good old backpropagation (TL 1.2) in deep but otherwise standard NN, without unsupervised pre-training, and without convolution (but with training pattern deformations). This was made possible mainly by boosting computing power through a fast GPU implementation [17]. (A year later, first human-competitive performance on MNIST was achieved by a deep MCMPCNN (TL 1.12) [22].)

1.12 2011: MPCNN on GPU / First Superhuman Visual Pattern Recognition

In 2011, Ciresan et al. introduced supervised GPU-based Max-Pooling CNN or Convnets (MPCNN) [18], today used by most if not all feedforward competition-winning deep NN (TL 1.13, TL 1.14). The first superhuman visual pattern recognition in a controlled competition (traffic signs  [87]) was achieved  [20, 19] (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) GPU-MPCNN [18, 19], the current gold standard for deep feedforward NN.

1.13 2012: First Contests Won on Object Detection and Image Segmentation

2012 saw the first Deep learning system (a GPU-MCMPCNN [18, 19], TL 1.12) to win a contest on visual object detection in large images (as opposed to mere recognition/classification): the ICPR 2012 Contest on Mitosis Detection in Breast Cancer Histological Images  [2, 74, 16]. An MC (TL 1.12) variant of a GPU-MPCNN also achieved best results on the ImageNet classification benchmark [51]. 2012 also saw the first pure image segmentation contest won by Deep Learning (again through a GPU-MCMPCNN), namely, the ISBI 2012 Challenge on segmenting neuronal structures  [3, 15]. This was the 8th international pattern recognition contest won by my team since 2009 [1].

1.14 2013: More Contests and Benchmark Records

In 2013, a new TIMIT phoneme recognition record was set by deep LSTM RNN  [38] (TL 1.8, TL 1.10). A new record [24] on the ICDAR Chinese handwriting recognition benchmark (over 3700 classes) was set on a desktop machine by a GPU-MCMPCNN with almost human performance. The MICCAI 2013 Grand Challenge on Mitosis Detection was won by a GPU-MCMPCNN  [88, 16]. Deep GPU-MPCNN [18] also helped to achieve new best results on ImageNet classification [95] and PASCAL object detection [34]. Additional contests are mentioned in the web pages of the Swiss AI Lab IDSIA, the University of Toronto, NY University, and the University of Montreal.

2 Acknowledgments

Drafts/revisions of this paper have been published since 20 Sept 2013 in my massive open peer review web site (also under 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.


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