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Deep Learning Research Landscape & Roadmap in a Nutshell: Past, Present

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Deep Learning Research Landscape & Roadmap in a Nutshell: Past, Present Deep learning research landscape & roadmap in a nutshell: past, present and future - Towards deep cortical learning Aras R. Dargazany August 7, 2019 Abstract The past, present and future of deep learning is presented in this work. Given this landscape & roadmap, we predict that deep cortical learning will be the convergence of deep learning & cortical learning which builds an artificial cortical column ultimately. 1 Past: Deep learning inspirations Deep learning horizon, landscape and research roadmap in nutshell is presented in this figure1. The historical development and timeline of deep learning & neural network is separately illustrated arXiv:1908.02130v1 [cs.NE] 30 Jul 2019 Figure 1: Deep learning research landscape & roadmap: past, present, future. The future is highlighted as deep cortical learning. in figure2. The Origin of neural nets [WR17] is thoroughly reviewed in terms of the evolutionary history of deep learning models. Vernon Mountcastle discovery of cortical columns in somatosen- sory cortex [Mou97] was a breakthrough in brain science. The big bang was the discovery of Hubel & Wiesel of simple cells and complex cell in visual cortex [HW59] which won the Nobel prize for this discovery in 1981. This work was heavily founded on Vernon Mountcastle discovery of cortical columns in somatosensory cortex [Mou97]. After the discovery of Hubel & Wiesel, Fukushima proposed a pattern recognition architecture based on the simple cell and complex cell discovery, known as NeoCognitron [FM82]. In this work, a deep neural network was proposed using simple cell layer and complex cell layer repeatedly. In 80s and maybe a bit earlier backpropagation have been proposed by multiple people but the first time it was well-explained and applied for learning neural nets was done by Hinton and his colleagues in 1987 [RHW86]. 1 Figure 2: Neural nets origin, timeline & history made by Favio Vazquez 2 Present: Deep learning by LeCun, Bengio and Hinton Convolutional nets was invented by LeCun [LBD+89] which led to deep learning conspiracy which also started by the three founding fathers of the field: LeCun, Bengio and Hinton [LBH15]. The main hype in deep learning happened in 2012 when the state-of-the-art result in Imagenet classi- fication and TIMIT speech recognition task were dramatically reduced using an end-to-end deep convolutional network [KSH12] and deep belief net [HDY+12]. The power of deep learning is scalability and the ability to learn in an end-to-end fashion. In this sense, deep learning architectures are capable of learning big datasets such as Imagenet [KSH12, GDG+17] and TIMIT using multiple GPUs in an end-to-end fashion meaning directly from raw inputs, all the way the desired outputs. Alexnet [KSH12] used two GPUs for Imagenet classification which is a very big dataset of images, almost 1.5 million images of size 215x215. Kaiming He et al. [GDG+17] proposed a highly scalable approach for training on Image using 256 GPUs for almost an hour which shows an amazingly powerful approach based stochastic gradient descent for applying big cluster of GPUs on huge datasets. Very many application domains have been revolutionized using deep learning architectures such as image classifications [KSH12], machine translation [WSC+16, JSL+16], speech recognition [HDY+12], and robotics [MKS+15]. The Nobel Prize in Physiology or Medicine 2014 was given to John O’Keefe, May-Britt Moser and Edvard I. Moser “for their discoveries of cells that constitute a positioning system in the brain.” [Bur14]. This study of cognitive neuroscience shed light on how the world is repre- sented within the brain. Hinton’s Capsule network [SFH17] and Hawkins’ cortical learning al- gorithm [HAD11] are highly inspired by this Nobel-prize winning work [Bur14]. 3 Future: Brain-plausible deep learning & cortical learning algorithms The main direction and inclination in the deep learning for future is the ability to bridge the gap between the cortical architecture and deep learning architectures, specifically convolutional nets. In this quest, Hinton proposed capsule network [SFH17] as an effort to get rid of pooling layers and replace it with capsules which are highly inspired bu cortical mini-columns in cortical columns and layers and include the location information or pose information of parts. Another important quest in deep learning is understanding the biological root of learning in our brain, specifically in our cortex. Backpropagation is not biologically inspired and plausible. Hinton and the other founding fathers of deep learning have been trying to understand how backprop 2 might be feasible biologically in brain. Feedback alignment [LCTA16] and spike time-dependent plasticity or STDP-based backprop [BSR+18] are some of the works which have been done by Timothy Lillicrap, Blake Richards, and Hinton in order to model backprop biologically based on the pyramidal neuron in the cortex. In the far future, the main goal should be the merge of two very independent quest to build cortical structure in our brain: The first one is heavily target by the big and active deep learning community; The second one is targeted independently and neuroscientifically by Numenta and Geoff Hawkins [HAD11]. These people argue that the cortical structure and our neocortex is the main source of our intelligence and for building a true intelligent machine, we should be able to reconstruct the cortex and to do so, we should first focus more on the cortex and understand what cortex is made out of. 4 Finale: Deep cortical learning as the merge of deep learning and cortical learning By merging deep learning and cortical learning, a very more focused and detailed architectures, named deep cortical learning might be created. We might be able to understand and reconstruct the cortical structure with much more accuracy and have a better idea what the true intelligence is and how artificial general intelligence or AGI might be reproducible. Deep cortical learning might be the algorithm behind one cortical column in the neocortex. References [BSR+18] Sergey Bartunov, Adam Santoro, Blake Richards, Luke Marris, Geoffrey E Hinton, and Timothy Lillicrap. Assessing the scalability of biologically-motivated deep learning algorithms and architectures. In Advances in Neural Information Processing Systems, pages 9368–9378, 2018. [Bur14] Neil Burgess. The 2014 nobel prize in physiology or medicine: a spatial model for cognitive neuroscience. Neuron, 84(6):1120–1125, 2014. [FM82] Kunihiko Fukushima and Sei Miyake. Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. In Competition and cooperation in neural nets, pages 267–285. Springer, 1982. [GDG+17] Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He. Accurate, large minibatch sgd: training imagenet in 1 hour. arXiv preprint arXiv:1706.02677, 2017. [HAD11] Jeff Hawkins, Subutai Ahmad, and D Dubinsky. Hierarchi- cal temporal memory including htm cortical learning algorithms. Technical report, Numenta, Inc. http://www.numenta.com/htm- overview/education/HTM/CorticalLearningAlgorithms.pdf, 2011. [HDY+12] Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine, 29(6):82–97, 2012. [HW59] David H Hubel and Torsten N Wiesel. Receptive fields of single neurones in the cat’s striate cortex. The Journal of physiology, 148(3):574–591, 1959. [JSL+16] Melvin Johnson, Mike Schuster, Quoc V Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, et al. Google’s multilingual neural machine translation system: Enabling zero-shot trans- lation. arXiv preprint arXiv:1611.04558, 2016. [KSH12] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems, pages 1097–1105, 2012. 3 [LBD+89] Yann LeCun, Bernhard Boser, John S Denker, Donnie Henderson, Richard E Howard, Wayne Hubbard, and Lawrence D Jackel. Backpropagation applied to handwritten zip code recognition. Neural computation, 1(4):541–551, 1989. [LBH15] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature, 521(7553):436–444, 2015. [LCTA16] Timothy P Lillicrap, Daniel Cownden, Douglas B Tweed, and Colin J Akerman. Ran- dom synaptic feedback weights support error backpropagation for deep learning. Nature communications, 7:13276, 2016. [MKS+15] Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Andrei A Rusu, Joel Veness, Marc G Bellemare, Alex Graves, Martin Riedmiller, Andreas K Fidjeland, Georg Ostrovski, et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015. [Mou97] Vernon B Mountcastle. The columnar organization of the neocortex. Brain: a journal of neurology, 120(4):701–722, 1997. [RHW86] David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams. Learning representa- tions by back-propagating errors. Nature, 323:533–536, 1986. [SFH17] Sara Sabour, Nicholas Frosst, and Geoffrey E Hinton. Dynamic routing between cap- sules. In Advances in Neural Information Processing Systems, pages 3859–3869, 2017. [WR17] Haohan Wang and Bhiksha Raj. On the origin of deep learning. arXiv preprint arXiv:1702.07800, 2017. [WSC+16] Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, et al. Google’s neural machine translation system: Bridging the gap between human and machine translation. arXiv preprint arXiv:1609.08144, 2016. 4.
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