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Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations. Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020) (2020). at <https://proceedings.neurips.cc/paper/2020/hash/98b17f068d5d9b7668e19fb8ae470841-Abstract.html>
Social interaction networks in the primate brain. Current Opinion in Neurobiology 65, 49 - 58 (2020).
The speed of human social interaction perception. NeuroImage 116844 (2020). doi:10.1016/j.neuroimage.2020.116844
Stable Foundations for Learning: a framework for learning theory (in both the classical and modern regime). (2020). Original file (584.54 KB) Corrected typos and details of "equivalence" CV stability and expected error for interpolating machines. Added Appendix on SGD. (905.29 KB) Edited Appendix on SGD. (909.19 KB) Deleted Appendix. Corrected typos etc (880.27 KB) Added result about square loss and min norm (898.03 KB)
Temporal information for action recognition only needs to be integrated at a choice level in neural networks and primates . COSYNE (2020).
Theoretical issues in deep networks. Proceedings of the National Academy of Sciences 201907369 (2020). doi:10.1073/pnas.1907369117 PNASlast.pdf (915.3 KB)
A theory of learning to infer. Psychological Review 127, 412 - 441 (2020).
ThreeDWorld: A Platform for Interactive Multi-Modal Physical Simulation. arXiv (2020). at <https://arxiv.org/abs/2007.04954> 2007.04954.pdf (7.06 MB)
ThreeDWorld (TDW): A High-Fidelity, Multi-Modal Platform for Interactive Physical Simulation. (2020). at <http://www.threedworld.org/>
Time-dependent discrimination advantages for harmonic sounds suggest efficient coding for memory. Proceedings of the National Academy of Sciences 117, 32169 - 32180 (2020).
Toward human-like object naming in artificial neural systems . International Conference on Learning Representations (ICLR 2020), Bridging AI and Cognitive Science workshop (2020).
Using task-optimized neural networks to understand why brains have specialized processing for faces . Computational and Systems Neurosciences (2020).
What can human minimal videos tell us about dynamic recognition models?. International Conference on Learning Representations (ICLR 2020) (2020). at <https://baicsworkshop.github.io/pdf/BAICS_1.pdf> Authors' final version (516.09 KB)
Why Are Face and Object Processing Segregated in the Human Brain? Testing Computational Hypotheses with Deep Convolutional Neural Networks . Conference on Cognitive Computational Neuroscience (2020).
XDream: Finding preferred stimuli for visual neurons using generative networks and gradient-free optimization. PLOS Computational Biology 16, e1007973 (2020). gk7791.pdf (2.39 MB)
Analysis of Macaque Monkeys’ Social and Physical Interaction Processing with Eye tracking Data. The Rockefeller University 2019 Summer Science Research Program (SSRP) (2019).
An analysis of training and generalization errors in shallow and deep networks. (2019). CBMM-Memo-098.pdf (687.36 KB) CBMM Memo 098 v4 (08/2019) (2.63 MB)
Are topographic deep convolutional neural networks better models of the ventral visual stream?. Conference on Cognitive Computational Neuroscience (2019).
Beating SGD Saturation with Tail-Averaging and Minibatching. Neural Information Processing Systems (NeurIPS 2019) (2019). 9422-beating-sgd-saturation-with-tail-averaging-and-minibatching.pdf (389.35 KB)
Biologically-plausible learning algorithms can scale to large datasets. International Conference on Learning Representations, (ICLR 2019) (2019). gk7779.pdf (721.53 KB)
Blind Constant Modulus Multiuser Detection via Low-Rank Approximation. IEEE Signal Processing Letters 1 - 1 (2019). doi:10.1109/LSP.9710.1109/LSP.2019.2918001
Brain Signals Localization by Alternating Projections. arXiv (2019). CBMM-Memo-099.pdf (421.67 KB)
Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019). 2019-10-28 NeurIPS-camera_ready.pdf (1.88 MB)
Choosing a Transformative Experience . Cognitive Sciences Society (2019).
Constant modulus algorithms via low-rank approximation. Signal Processing 160, 263 - 270 (2019).