All Publications

2024

F. Montagna, Mastakouri, A. A., Eulig, E., Noceti, N., Rosasco, L., Janzing, D., Aragam, B., and Locatello, F., “Assumption violations in causal discovery and the robustness of score matching”, in 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2024.
CBMM Related
CBMM Memo No.
151
A. Pinto, Rangamani, A., and Poggio, T., “On Generalization Bounds for Neural Networks with Low Rank Layers”. 2024.PDF icon CBMM-Memo-151.pdf (697.31 KB)
CBMM Funded
CBMM Memo No.
150
L. Ziyin, Chuang, I., Galanti, T., and Poggio, T., “Formation of Representations in Neural Networks”. 2024.PDF icon CBMM-Memo-150.pdf (4.03 MB)
CBMM Funded
CBMM Memo No.
149
Y. Gan, Galanti, T., Poggio, T., and Malach, E., “On the Power of Decision Trees in Auto-Regressive Language Modeling”. 2024.PDF icon CBMM-Memo-149.pdf (2.11 MB)
CBMM Funded
CBMM Memo No.
148
Y. Gan and Poggio, T., “For HyperBFs AGOP is a greedy approximation to gradient descent”. 2024.PDF icon CBMM-Memo-148.pdf (1.06 MB)
CBMM Funded
T. Poggio and Fraser, M., “Compositional sparsity of learnable functions”, Bulletin of the American Mathematical Society, vol. 61, pp. 438-456, 2024.
CBMM Funded
F. Ceola, Rosasco, L., Natale, L., and Ceola, F., “RESPRECT: Speeding-up Multi-Fingered Grasping With Residual Reinforcement LearningRESPRECT: Speeding-Up Multi-Fingered Grasping With Residual Reinforcement Learning_supp1-3363532.mp4”, IEEE Robotics and Automation Letters, vol. 9, no. 4, pp. 3045 - 3052, 2024.
CBMM Funded
K. Mahowald, Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., and Fedorenko, E., “Dissociating language and thought in large language models”, Trends in Cognitive Sciences, vol. 28, no. 6, pp. 517 - 540, 2024.
CBMM Related
CBMM Memo No.
145
T. Poggio and Fraser, M., “Compositional Sparsity of Learnable Functions”. 2024.PDF icon CBMM-Memo-145.pdf (1.25 MB)
CBMM Funded
P. Didier Alfano, Pastore, V. Paolo, Rosasco, L., and Odone, F., “Top-tuning: A study on transfer learning for an efficient alternative to fine tuning for image classification with fast kernel methods”, Image and Vision Computing, vol. 142, p. 104894, 2024.
CBMM Related
S. J. Gershman, “What have we learned about artificial intelligence from studying the brain?”, Biological Cybernetics, vol. 118, no. 1-2, pp. 1 - 5, 2024.
CBMM Funded
D. Mendoza-Halliday, Xu, H., Azevedo, F. A. C., and Desimone, R., “Dissociable neuronal substrates of visual feature attention and working memory”, Neuron, vol. 112, no. 5, pp. 850 - 863.e6, 2024.
CBMM Related
D. Mendoza-Halliday, Major, A. James, Lee, N., Lichtenfeld, M. J., Carlson, B., Mitchell, B., Meng, P. D., Xiong, Y., Westerberg, J. A., Jia, X., Johnston, K. D., Selvanayagam, J., Everling, S., Maier, A., Desimone, R., Miller, E. K., and Bastos, A. M., “A ubiquitous spectrolaminar motif of local field potential power across the primate cortexAbstract”, Nature Neuroscience, vol. 27, no. 3, pp. 547 - 560, 2024.
CBMM Related
E. M. Meyers, “NeuroDecodeR: a package for neural decoding in RData_Sheet_1.docx”, Frontiers in Neuroinformatics, vol. 17, 2024.
CBMM Funded
B. Peters, DiCarlo, J. J., Gureckis, T., Haefner, R., Isik, L., Tenenbaum, J., Konkle, T., Naselaris, T., Stachenfeld, K., Tavares, Z., Tsao, D., Yildirim, I., and Kriegeskorte, N., “How does the primate brain combine generative and discriminative computations in vision?”, arXiv, 2024.

2023

CBMM Memo No.
144
M. Xu, Galanti, T., Rangamani, A., Rosasco, L., Pinto, A., and Poggio, T., “The Janus effects of SGD vs GD: high noise and low rank”. 2023.PDF icon Updated with appendix showing empirically that the main results extend to deep nonlinear networks (2.95 MB)PDF icon Small updates...typos... (616.82 KB)
CBMM Funded
K. R. Allen, Smith, K. A., Bird, L. - A., Tenenbaum, J. B., Makin, T. R., and Cowie, D., “Lifelong learning of cognitive styles for physical problem-solving: The effect of embodied experienceAbstract”, Psychonomic Bulletin & Review, 2023.
CBMM Funded
T. Galanti, Xu, M., Galanti, L., and Poggio, T., “Norm-based Generalization Bounds for Sparse Neural Networks”, in NeurIPS 2023, New Orleans, 2023.PDF icon NeurIPS-2023-norm-based-generalization-bounds-for-sparse-neural-networks-Paper-Conference.pdf (577.69 KB)
CBMM Funded
G. Gaziv, Lee, M. J., and DiCarlo, J. J., “Strong and Precise Modulation of Human Percepts via Robustified ANNs”, in NeurIPS 2023, 2023.
CBMM Funded
G. Meanti*, Chatalic, A., Kostic, V. R., Novelli, P., Pontil, M., and Rosasco, L., “Estimating Koopman operators with sketching to provably learn large scale dynamical systems”, in 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2023.
CBMM Funded
M. Rando, Molinari, C., Villa, S., and Rosasco, L., “An Optimal Structured Zeroth-order Algorithm for Non-smooth Optimization”, in 37th Conference on Neural Information Processing Systems (NeurIPS 2023), 2023.
CBMM Funded
U. Singhal, Cheung, B., Chandra, K., Ragan-Kelley, J., Tenenbaum, J. B., Poggio, T., and Yu, S. X., “How to Guess a Gradient”, arXiv, 2023.
CBMM Funded
G. Tuckute, Feather, J., Boebinger, D., and McDermott, J. H., “Many but not all deep neural network audio models capture brain responses and exhibit correspondence between model stages and brain regions”, PLOS Biology, vol. 21, no. 12, p. e3002366, 2023.
CBMM Funded
Y. Xiang, Landy, J., Cushman, F. A., Vélez, N., and Gershman, S. J., “Actual and counterfactual effort contribute to responsibility attributions in collaborative tasks”, Cognition, vol. 241, p. 105609, 2023.
CBMM Funded
C. Yu, Burgess, N., Sahani, M., and Gershman, S. J., “Successor-Predecessor Intrinsic Exploration”, in Advances in Neural Information Processing Systems 36 (NeurIPS 2023), 2023.
CBMM Funded

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