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Laurent Itti | Jiaping Zhao | Bosco S. Tjan | Andrew M. Saxe | Tommaso Furlanello | Tommaso Furlanello | L. Itti | B. Tjan | Jiaping Zhao
[1] J. Knott. The organization of behavior: A neuropsychological theory , 1951 .
[2] K. H. Stauder. [Psychology of the child]. , 1953, Medizinische Klinik.
[3] W. Scoville,et al. LOSS OF RECENT MEMORY AFTER BILATERAL HIPPOCAMPAL LESIONS , 1957, Journal of neurology, neurosurgery, and psychiatry.
[4] D. Hubel,et al. EFFECTS OF VISUAL DEPRIVATION ON MORPHOLOGY AND PHYSIOLOGY OF CELLS IN THE CATS LATERAL GENICULATE BODY. , 1963, Journal of neurophysiology.
[5] Geoffrey E. Hinton. Learning distributed representations of concepts. , 1989 .
[6] Lawrence D. Jackel,et al. Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.
[7] Michael McCloskey,et al. Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .
[8] Robert Hecht-Nielsen,et al. Theory of the backpropagation neural network , 1989, International 1989 Joint Conference on Neural Networks.
[9] Jack Mostow,et al. Direct Transfer of Learned Information Among Neural Networks , 1991, AAAI.
[10] G. Reeke. The society of mind , 1991 .
[11] Marvin Minsky,et al. Society of Mind: A Response to Four Reviews , 1991, Artif. Intell..
[12] Yann LeCun,et al. Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..
[13] Rich Caruana,et al. Learning Many Related Tasks at the Same Time with Backpropagation , 1994, NIPS.
[14] James L. McClelland,et al. Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory. , 1995, Psychological review.
[15] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[16] Rich Caruana,et al. Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.
[17] R. French. Catastrophic forgetting in connectionist networks , 1999, Trends in Cognitive Sciences.
[18] J. Crutchfield,et al. Thermodynamic depth of causal states: Objective complexity via minimal representations , 1999 .
[19] C. Shalizi,et al. Causal architecture, complexity and self-organization in time series and cellular automata , 2001 .
[20] A. Ishai,et al. Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex , 2001, Science.
[21] Yoshua Bengio,et al. Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .
[22] Long Ji Lin,et al. Self-improving reactive agents based on reinforcement learning, planning and teaching , 1992, Machine Learning.
[23] Rich Caruana,et al. Model compression , 2006, KDD '06.
[24] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[25] Klaus-Robert Müller,et al. Efficient BackProp , 2012, Neural Networks: Tricks of the Trade.
[26] Pascal Vincent,et al. Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] Surya Ganguli,et al. Learning hierarchical categories in deep neural networks , 2013, CogSci.
[28] Ha Hong,et al. Hierarchical Modular Optimization of Convolutional Networks Achieves Representations Similar to Macaque IT and Human Ventral Stream , 2013, NIPS.
[29] Yoshua Bengio,et al. An Empirical Investigation of Catastrophic Forgeting in Gradient-Based Neural Networks , 2013, ICLR.
[30] Yoshua Bengio,et al. How transferable are features in deep neural networks? , 2014, NIPS.
[31] Surya Ganguli,et al. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks , 2013, ICLR.
[32] Rich Caruana,et al. Do Deep Nets Really Need to be Deep? , 2013, NIPS.
[33] Andrea Vedaldi,et al. MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.
[34] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[35] Joshua B. Tenenbaum,et al. Deep Convolutional Inverse Graphics Network , 2015, NIPS.
[36] Jianmin Wang,et al. Learning Multiple Tasks with Deep Relationship Networks , 2015, ArXiv.
[37] Matthew Richardson,et al. Blending LSTMs into CNNs , 2015, ICLR 2016.
[38] Matthew Richardson,et al. Compressing LSTMs into CNNs , 2015, ArXiv.
[39] Rauf Izmailov,et al. Learning using privileged information: similarity control and knowledge transfer , 2015, J. Mach. Learn. Res..
[40] Peter Kulchyski. and , 2015 .
[41] Shane Legg,et al. Human-level control through deep reinforcement learning , 2015, Nature.
[42] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[43] Razvan Pascanu,et al. Policy Distillation , 2015, ICLR.
[44] Ruslan Salakhutdinov,et al. Actor-Mimic: Deep Multitask and Transfer Reinforcement Learning , 2015, ICLR.
[45] Tomas Mikolov,et al. A Roadmap Towards Machine Intelligence , 2015, CICLing.
[46] Matthew Richardson,et al. Do Deep Convolutional Nets Really Need to be Deep (Or Even Convolutional)? , 2016, ArXiv.
[47] Shie Mannor,et al. Graying the black box: Understanding DQNs , 2016, ICML.
[48] Joshua B. Tenenbaum,et al. Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.
[49] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[50] Ali Borji,et al. iLab-20M: A Large-Scale Controlled Object Dataset to Investigate Deep Learning , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[51] Tomaso A. Poggio,et al. Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex , 2016, ArXiv.
[52] Joshua B. Tenenbaum,et al. Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivation , 2016, NIPS.
[53] Bernhard Schölkopf,et al. Unifying distillation and privileged information , 2015, ICLR.
[54] Shie Mannor,et al. A Deep Hierarchical Approach to Lifelong Learning in Minecraft , 2016, AAAI.
[55] Matthew Richardson,et al. Do Deep Convolutional Nets Really Need to be Deep and Convolutional? , 2016, ICLR.