Publication
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Invariant recognition drives neural representations of action sequences. PLoS Comp. Bio (2017).
Invariant recognition drives neural representations of action sequences. PLOS Computational Biology 13, e1005859 (2017).
journal.pcbi_.1005859.pdf (9.24 MB)

Computational and Cognitive Neuroscience of Vision 85-104 (Springer, 2017).
Musings on Deep Learning: Properties of SGD. (2017).
CBMM Memo 067 v2 (revised 7/19/2017) (5.88 MB)
CBMM Memo 067 v3 (revised 9/15/2017) (5.89 MB)
CBMM Memo 067 v4 (revised 12/26/2017) (5.57 MB)



Object-Oriented Deep Learning. (2017).
CBMM-Memo-070.pdf (963.54 KB)

Pruning Convolutional Neural Networks for Image Instance Retrieval. (2017). at <https://arxiv.org/abs/1707.05455>
1707.05455.pdf (143.46 KB)

Representation Learning from Orbit Sets for One-shot Classification. AAAI Spring Symposium Series, Science of Intelligence (2017). at <https://www.aaai.org/ocs/index.php/SSS/SSS17/paper/view/15357>
Symmetry Regularization. (2017).
CBMM-Memo-063.pdf (6.1 MB)

Theory II: Landscape of the Empirical Risk in Deep Learning. (2017).
CBMM Memo 066_1703.09833v2.pdf (5.56 MB)

Theory of Deep Learning IIb: Optimization Properties of SGD. (2017).
CBMM-Memo-072.pdf (3.66 MB)

Theory of Deep Learning III: explaining the non-overfitting puzzle. (2017).
CBMM-Memo-073.pdf (2.65 MB)
CBMM Memo 073 v2 (revised 1/15/2018) (2.81 MB)
CBMM Memo 073 v3 (revised 1/30/2018) (2.72 MB)
CBMM Memo 073 v4 (revised 12/30/2018) (575.72 KB)




View-Tolerant Face Recognition and Hebbian Learning Imply Mirror-Symmetric Neural Tuning to Head Orientation. Current Biology 27, 1-6 (2017).
When and Why Are Deep Networks Better Than Shallow Ones?. AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence (2017).
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review. International Journal of Automation and Computing 1-17 (2017). doi:10.1007/s11633-017-1054-2
art%3A10.1007%2Fs11633-017-1054-2.pdf (1.68 MB)

Bridging the Gaps Between Residual Learning, Recurrent Neural Networks and Visual Cortex. (2016).
CBMM Memo No. 047 (1.29 MB)

Deep Leaning: Mathematics and Neuroscience. A Sponsored Supplement to Science Brain-Inspired intelligent robotics: The intersection of robotics and neuroscience, 9-12 (2016).
Deep Learning: mathematics and neuroscience. (2016).
Deep Learning- mathematics and neuroscience.pdf (1.25 MB)

Deep vs. shallow networks : An approximation theory perspective. (2016).
Original submission, visit the link above for the updated version (960.27 KB)

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>
CBMM Memo 042 (3.03 MB)

Foveation-based Mechanisms Alleviate Adversarial Examples. (2016).
cbmm_memo_044.pdf (11.48 MB)

Group Invariant Deep Representations for Image Instance Retrieval. (2016).
CBMM-Memo-043.pdf (2.66 MB)

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)
