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Holographic Embeddings of Knowledge Graphs. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (2016).
1510.04935v2.pdf (360.65 KB)

How Important Is Weight Symmetry in Backpropagation?. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16) (Association for the Advancement of Artificial Intelligence, 2016).
liao-leibo-poggio.pdf (191.91 KB)

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>
On invariance and selectivity in representation learning. Information and Inference: A Journal of the IMA iaw009 (2016). doi:10.1093/imaiai/iaw009
imaiai.iaw009.full_.pdf (267.87 KB)

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. (2015).
modularity_dataset_ver1.tar.gz (36.14 MB)

The Invariance Hypothesis Implies Domain-Specific Regions in Visual Cortex. PLOS Computational Biology 11, e1004390 (2015).
journal.pcbi_.1004390.pdf (2.04 MB)

Invariant representations for action recognition in the visual system. Vision Sciences Society 15, (2015).
Invariant representations for action recognition in the visual system. Computational and Systems Neuroscience (2015).
I-theory on depth vs width: hierarchical function composition. (2015).
cbmm_memo_041.pdf (1.18 MB)
