Cognition-Based Deep Learning: Progresses and Perspectives

The human brain is composed of multiple modular subsystems, with a unique way interacting among each other. These subsystems have their own unique characteristics and interact to support cognitive functions such as memory, attention and cognitive control. Nowadays, deep learning methods based on the above-mentioned functions accompanied with knowledge are widely used to design more dynamic, robust and powerful systems. We first review and summarize the progresses of cognition-based deep neural networks, and how cognitive mechanisms can be applied to more brain-like neural networks. Then we propose a general framework for the design of cognition-based deep learning system. Although great efforts have been made in this field, cognition-based deep learning is still in its early age. We put forward the potential directions towards this field, such as associative memory in deep learning, interpretable network with cognitive mechanisms, and deep reinforcement learning based on cognitive science.

[1]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[2]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[3]  Razvan Pascanu,et al.  Overcoming catastrophic forgetting in neural networks , 2016, Proceedings of the National Academy of Sciences.

[4]  R. Venkatesh Babu,et al.  DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations , 2015, IEEE Transactions on Image Processing.

[5]  Diyi Yang,et al.  Hierarchical Attention Networks for Document Classification , 2016, NAACL.

[6]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[7]  Richard L. Lewis,et al.  The mind and brain of short-term memory. , 2008, Annual review of psychology.

[8]  Yann Dauphin,et al.  Convolutional Sequence to Sequence Learning , 2017, ICML.

[9]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[10]  Alex Graves,et al.  Playing Atari with Deep Reinforcement Learning , 2013, ArXiv.

[11]  Alex Graves,et al.  Recurrent Models of Visual Attention , 2014, NIPS.

[12]  Xiaolin Hu,et al.  Recurrent convolutional neural network for object recognition , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Eric P. Xing,et al.  Harnessing Deep Neural Networks with Logic Rules , 2016, ACL.

[14]  Nanning Zheng,et al.  Learning to Detect a Salient Object , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Truong Q. Nguyen,et al.  Context Matters: Refining Object Detection in Video with Recurrent Neural Networks , 2016, BMVC.

[16]  Wenpeng Yin,et al.  Comparative Study of CNN and RNN for Natural Language Processing , 2017, ArXiv.

[17]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[18]  John J. Hopfield,et al.  Dense Associative Memory for Pattern Recognition , 2016, NIPS.

[19]  Linda B. Smith,et al.  The importance of shape in early lexical learning , 1988 .

[20]  Lucila Ohno-Machado,et al.  Natural language processing: an introduction , 2011, J. Am. Medical Informatics Assoc..

[21]  Gang Wang,et al.  Recurrent Attentional Networks for Saliency Detection , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Herbert A. Simon,et al.  Cognitive Science: The Newest Science of the Artificial , 1980, Cogn. Sci..

[23]  Samuel Ritter,et al.  Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study , 2017, ICML.

[24]  Koray Kavukcuoglu,et al.  Visual Attention , 2020, Computational Models for Cognitive Vision.

[25]  Oriol Vinyals,et al.  Matching Networks for One Shot Learning , 2016, NIPS.

[26]  Yoshua Bengio,et al.  Show, Attend and Tell: Neural Image Caption Generation with Visual Attention , 2015, ICML.

[27]  Joshua B. Tenenbaum,et al.  Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.

[28]  Sinno Jialin Pan,et al.  Knowledge Transfer for Deep Reinforcement Learning with Hierarchical Experience Replay , 2017, AAAI.

[29]  Andrew W. Senior,et al.  Long short-term memory recurrent neural network architectures for large scale acoustic modeling , 2014, INTERSPEECH.

[30]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[31]  Ruslan Salakhutdinov,et al.  Gated-Attention Readers for Text Comprehension , 2016, ACL.

[32]  Marcus Rohrbach,et al.  Memory Aware Synapses: Learning what (not) to forget , 2017, ECCV.

[33]  Eric P. Xing,et al.  Deep Neural Networks with Massive Learned Knowledge , 2016, EMNLP.

[34]  Stefano Ermon,et al.  Label-Free Supervision of Neural Networks with Physics and Domain Knowledge , 2016, AAAI.

[35]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[36]  Tianming Liu,et al.  Predicting eye fixations using convolutional neural networks , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[37]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[38]  Yongqiang Wang,et al.  Simplifying long short-term memory acoustic models for fast training and decoding , 2016, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[39]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[40]  Wei Liu,et al.  SSD: Single Shot MultiBox Detector , 2015, ECCV.

[41]  Helgi Páll Helgason,et al.  General Attention Mechanism for Artificial Intelligence Systems , 2013 .

[42]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[43]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[44]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[45]  Ben Taskar,et al.  Posterior Regularization for Structured Latent Variable Models , 2010, J. Mach. Learn. Res..