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Deep vs. shallow networks: An approximation theory perspective. Analysis and Applications 14, 829 - 848 (2016).
Fast, invariant representation for human action in the visual system. (2016). at <http://arxiv.org/abs/1601.01358>
Holographic Embeddings of Knowledge Graphs. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (2016).
How Important Is Weight Symmetry in Backpropagation?. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (Association for the Advancement of Artificial Intelligence, 2016).
How Important Is Weight Symmetry in Backpropagation?. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (2016). at <https://cbmm.mit.edu/sites/default/files/publications/liao-leibo-poggio.pdf>
Introduction Special issue: Deep learning. Information and Inference 5, 103-104 (2016).
On invariance and selectivity in representation learning. Information and Inference: A Journal of the IMA iaw009 (2016). doi:10.1093/imaiai/iaw009
Learning Functions: When Is Deep Better Than Shallow. (2016). at <https://arxiv.org/pdf/1603.00988v4.pdf>
Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval. arXiv.org (2016). at <https://arxiv.org/abs/1603.04595>
Neural Tuning Size in a Model of Primate Visual Processing Accounts for Three Key Markers of Holistic Face Processing. Public Library of Science | PLoS ONE 1(3): e0150980, (2016).
From Neuron to Cognition via Computational Neuroscience (The MIT Press, 2016). at <https://mitpress.mit.edu/neuron-cognition>
Streaming Normalization: Towards Simpler and More Biologically-plausible Normalizations for Online and Recurrent Learning. (2016).
Turing++ Questions: A Test for the Science of (Human) Intelligence. AI Magazine 37 , 73-77 (2016).
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. (2016).
Visual Cortex and Deep Networks: Learning Invariant Representations. 136 (The MIT Press, 2016). at <https://mitpress.mit.edu/books/visual-cortex-and-deep-networks>
Discriminative Template Learning in Group-Convolutional Networks for Invariant Speech Representations. INTERSPEECH-2015 (International Speech Communication Association (ISCA), 2015). at <http://www.isca-speech.org/archive/interspeech_2015/i15_3229.html>
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLOS Computational Biology 11, e1004390 (2015).