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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> 1603.04595.pdf (2.9 MB)
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). journal.pone_.0150980.PDF (384.15 KB)
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). CBMM-Memo-057.pdf (1.27 MB)
Theory I: Why and When Can Deep Networks Avoid the Curse of Dimensionality?. (2016). CBMM-Memo-058v1.pdf (2.42 MB) CBMM-Memo-058v5.pdf (2.45 MB) CBMM-Memo-058-v6.pdf (2.74 MB) Proposition 4 has been deleted (2.75 MB)
Turing++ Questions: A Test for the Science of (Human) Intelligence. AI Magazine 37 , 73-77 (2016). Turing_Plus_Questions.pdf (424.91 KB)
View-tolerant face recognition and Hebbian learning imply mirror-symmetric neural tuning to head orientation. (2016). faceMirrorSymmetry_memo_ver01.pdf (3.93 MB)
Visual Cortex and Deep Networks: Learning Invariant Representations. 136 (The MIT Press, 2016). at <https://mitpress.mit.edu/books/visual-cortex-and-deep-networks>
Deep Convolutional Networks are Hierarchical Kernel Machines. (2015). CBMM Memo 035_rev5.pdf (975.65 KB)
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>
Holographic Embeddings of Knowledge Graphs. (2015). holographic-embeddings.pdf (677.87 KB)
How Important is Weight Symmetry in Backpropagation?. (2015). 1510.05067v3.pdf (615.32 KB)
On Invariance and Selectivity in Representation Learning. (2015). CBMM Memo No. 029 (812.07 KB)
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLOS Computational Biology 11, e1004390 (2015). journal.pcbi_.1004390.pdf (2.04 MB)
The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. (2015). modularity_dataset_ver1.tar.gz (36.14 MB)
Invariant representations for action recognition in the visual system. Computational and Systems Neuroscience (2015).
Invariant representations for action recognition in the visual system. Vision Sciences Society 15, (2015).
I-theory on depth vs width: hierarchical function composition. (2015). cbmm_memo_041.pdf (1.18 MB)
Learning with a Wasserstein Loss. Advances in Neural Information Processing Systems (NIPS 2015) 28 (2015). at <http://arxiv.org/abs/1506.05439> Learning with a Wasserstein Loss_1506.05439v2.pdf (2.57 MB)
Learning with Group Invariant Features: A Kernel Perspective. NIPS 2015 (2015). at <https://papers.nips.cc/paper/5798-learning-with-group-invariant-features-a-kernel-perspective> LearningInvarianceKernel_NIPS2015.pdf (292.18 KB)
Notes on Hierarchical Splines, DCLNs and i-theory. (2015). CBMM Memo 037 (1.83 MB)
A Science of Intelligence . (2015). A Science of Intelligence.pdf (659.5 KB)
Unsupervised learning of invariant representations. Theoretical Computer Science (2015). doi:10.1016/j.tcs.2015.06.048