All Publications

2026

T. Poggio, “Associative Memory as the Core of Intelligence in Technology and Evolution”. 2026.PDF icon Review_On_Associative_Memories-14.pdf (245.78 KB)
CBMM Funded
T. Poggio, “A Perspective: Sparse Compositionality and Efficiently Computable Intelligence”. 2026.PDF icon Perspective_SPCOMP-9.pdf (170.23 KB)
CBMM Funded

2025

CBMM Memo No.
159
D. A. Danhofer, D’Ascenzo, D., Dubach, R., and Poggio, T., “Position: A Theory of Deep Learning Must Include Compositional Sparsity”. 2025.PDF icon CBMM Memo 159.pdf (676.35 KB)
CBMM Funded
CBMM Memo No.
158
R. Dubach, Abdallah, M. S., and Poggio, T., “Multiplicative Regularization Generalizes Better Than Additive Regularization”. 2025.PDF icon CBMM Memo 158.pdf (4.8 MB)
CBMM Funded
R. T. Pramod, Mieczkowski, E., Fang, C. X., Tenenbaum, J. B., and Kanwisher, N., “Decoding predicted future states from the brain’s “physics engine””, Science Advances, vol. 11, no. 22, 2025.
CBMM Funded
CBMM Memo No.
156
T. A. Poggio and Xu, M., “On efficiently computable functions, deep networks and sparse compositionality”. 2025.PDF icon Deep_sparse_networks_approximate_efficiently_computable_functions.pdf (223.15 KB)
CBMM Funded
S. Bono, Madan, S., Grover, I., Yasueda, M., Breazeal, C., Pfister, H., and Kreiman, G., “The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function”. 2025.
CBMM Related
K. Tiwary, Young, A., Tasneem, Z., Klinghoffer, T., Dave, A., Poggio, T., Nilsson, D. - E., Cheung, B., and Raskar, R., “What if Eye..? Computationally Recreating Vision Evolution”, arXiv, 2025.PDF icon 2501.15001v1.pdf (5.2 MB)
CBMM Funded

2024

CBMM Memo No.
152
Q. Liao, Ziyin, L., Gan, Y., Cheung, B., Harnett, M., and Poggio, T., “Self-Assembly of a Biologically Plausible Learning Circuit”. 2024.PDF icon CBMM-Memo-152.pdf (1.84 MB)
CBMM Funded
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
S. Madan, Xiao, W., Cao, M., Pfister, H., Livingstone, M. S., and Kreiman, G., “Benchmarking Out-of-Distribution Generalization Capabilities of DNN-based Encoding Models for the Ventral Visual Cortex”, in NeurIPS 2024, 2024.
CBMM Related
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 This is an update of the AMS paper (230.72 KB)
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.

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