All Publications

2023

Z. Sage Chen and Wilson, M. A., “How our understanding of memory replay evolves”, Journal of Neurophysiology, vol. 129, no. 3, pp. 552 - 580, 2023.
CBMM Related
M. J. Lee and DiCarlo, J. J., “An empirical assay of view-invariant object learning in humans and comparison with baseline image-computable models”, bioRxiv, 2023.
CBMM Related
X. Puig, Shu, T., Tenenbaum, J. B., and Torralba, A., “NOPA: Neurally-guided Online Probabilistic Assistance for Building Socially Intelligent Home Assistants”, arXiv, 2023.
CBMM Related
A. Rangamani, Rosasco, L., and Poggio, T., “For interpolating kernel machines, minimizing the norm of the ERM solution maximizes stability”, Analysis and Applications, vol. 21, no. 01, pp. 193 - 215, 2023.
CBMM Funded
Y. Xiao, Chou, C. - C., Cosgrove, G. Rees, Crone, N. E., Stone, S., Madsen, J. R., Reucroft, I., Shih, Y. - C., Weisholtz, D., Yu, H. - Y., Anderson, W. S., and Kreiman, G., “Cross-task specificity and within-task invariance of cognitive control processes”, Cell Reports, vol. 42, no. 1, p. 111919, 2023.PDF icon PIIS2211124722018174.pdf (3.97 MB)
CBMM Funded
M. Xu, Rangamani, A., Liao, Q., Galanti, T., and Poggio, T., “Dynamics in Deep Classifiers trained with the Square Loss: normalization, low rank, neural collapse and generalization bounds”, Research, 2023.PDF icon research.0024.pdf (4.05 MB)
CBMM Funded
Y. Zhang, Aghajan, Z. M., Ison, M., Lu, Q., Tang, H., Kalender, G., Monsoor, T., Zheng, J., Kreiman, G., Roychowdhury, V., and Fried, I., “Decoding of human identity by computer vision and neuronal vision”, Scientific Reports, vol. 13, no. 1, 2023.PDF icon s41598-022-26946-w.pdf (1.88 MB)
CBMM Funded
Y. Zhang, Aghajan, Z. M., Ison, M., Lu, Q., Tang, H., Kalender, G., Monsoor, T., Zheng, J., Kreiman, G., Roychowdhury, V., and Fried, I., “Decoding of human identity by computer vision and neuronal visionAbstract”, Scientific Reports, vol. 13, no. 1, 2023.
CBMM Funded
S. Villa, Matet, S., Vũ, B. Công, and Rosasco, L., “Implicit regularization with strongly convex bias: Stability and acceleration”, Analysis and Applications, vol. 21, no. 01, pp. 165 - 191, 2023.
CBMM Related
CBMM Memo No.
140
T. Galanti, Siegel, Z., Gupte, A., and Poggio, T., “SGD and Weight Decay Provably Induce a Low-Rank Bias in Deep Neural Networks”. 2023.PDF icon Low-rank bias.pdf (2.38 MB)
CBMM Funded
CBMM Memo No.
139
T. Galanti, Xu, M., Galanti, L., and Poggio, T., “Norm-Based Generalization Bounds for Compositionally Sparse Neural Networks”. 2023.PDF icon Norm-based bounds for convnets.pdf (1.2 MB)
CBMM Funded

2022

J. Bill, Gershman, S. J., and Drugowitsch, J., “Visual motion perception as online hierarchical inference”, Nature Communications, vol. 13, no. 1, 2022.
CBMM Funded
E. M. Meyers, “NeuroDecodeR: A package for neural decoding analyses in R”, bioRxiv, 2022.
CBMM Funded
T. Pouncy and Gershman, S. J., “Inductive biases in theory-based reinforcement learning”, Cognitive Psychology, vol. 138, p. 101509, 2022.
CBMM Funded
Y. Tazi, Berger, M., and Freiwald, W. A., “Towards an objective characterization of an individual's facial movements using Self-Supervised Person-Specific-Models”, arXiv, 2022.
CBMM Funded
H. S. Wirtshafter and Wilson, M. A., “Artificial intelligence insights into hippocampal processing”, Frontiers in Computational Neuroscience, vol. 16, 2022.
CBMM Related
M. Zhang, Armendariz, M., Xiao, W., Rose, O., Bendtz, K., Livingstone, M., Ponce, C., and Kreiman, G., “Look twice: A generalist computational model predicts return fixations across tasks and species”, PLOS Computational Biology, vol. 18, no. 11, p. e1010654, 2022.PDF icon journal.pcbi_.1010654.pdf (4.51 MB)
CBMM Funded
CBMM Memo No.
138
T. Poggio, “How Deep Sparse Networks Avoid the Curse of Dimensionality: Efficiently Computable Functions are Compositionally Sparse”. 2022.PDF icon v1.0 (984.15 KB)PDF icon v5.7 adding in context learning etc (1.16 MB)
CBMM Funded
C. Xu, Yang, S., Galanti, T., Wu, B., Yue, X., Zhai, B., Zhan, W., Vajda, P., Keutzer, K., and Tomizuka, M., Lecture Notes in Computer ScienceComputer Vision – ECCV 2022Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models, vol. 13697. Cham: Springer Nature Switzerland, 2022, pp. 638 - 656.
CBMM Funded
A. Marliawaty Bagus, Marques, T., Sanghavi, S., DiCarlo, J. J., and Schrimpf, M., “Primate Inferotemporal Cortex Neurons Generalize Better to Novel Image Distributions Than Analogous Deep Neural Networks Units”, in NeurIPS, 2022.
CBMM Related
S. Casper, Nadeau, M., Hadfield-Menell, D., and Kreiman, G., “Robust Feature-Level Adversaries are Interpretability Tools”, NeurIPS. New Orleans, Louisiana, 2022.PDF icon 8789_robust_feature_level_adversari.pdf (3.79 MB)
CBMM Funded
M. Khosla, N. Murty, A. Ratan, and Kanwisher, N., “A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition”, Current Biology, vol. 32, no. 19, pp. 4159 - 4171.e9, 2022.
CBMM Related
M. Letizia, Losapio, G., Rando, M., Grosso, G., Wulzer, A., Pierini, M., Zanetti, M., and Rosasco, L., “Learning new physics efficiently with nonparametric methodsAbstract”, The European Physical Journal C, vol. 82, no. 10, 2022.
CBMM Related
S. Liu, Pepe, B., Kumar, M. Ganesh, Ullman, T. D., Tenenbaum, J. B., and Spelke, E. S., “Dangerous Ground: One-Year-Old Infants are Sensitive to Peril in Other Agents’ Action PlansAbstract”, Open Mind, vol. 6, pp. 211 - 231, 2022.
CBMM Funded
S. J. Gershman and Burke, T., “Mental control of uncertainty”, Cognitive, Affective, & Behavioral Neuroscience, vol. 23, no. 3, pp. 465 - 475, 2022.
CBMM Funded

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