Publication

Found 195 results
Author Title Type [ Year(Desc)]
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2023
Galanti, T., Xu, M., Galanti, L. & Poggio, T. Norm-Based Generalization Bounds for Compositionally Sparse Neural Networks. (2023).PDF icon Norm-based bounds for convnets.pdf (1.2 MB)
Galanti, T., Xu, M., Galanti, L. & Poggio, T. Norm-Based Generalization Bounds for Compositionally Sparse Neural Networks. (2023).PDF icon Norm-based bounds for convnets.pdf (1.2 MB)
Galanti, T., Xu, M., Galanti, L. & Poggio, T. Norm-based Generalization Bounds for Sparse Neural Networks. NeurIPS 2023 (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/8493e190ff1bbe3837eca821190b61ff-Paper-Conference.pdf>PDF icon NeurIPS-2023-norm-based-generalization-bounds-for-sparse-neural-networks-Paper-Conference.pdf (577.69 KB)
Galanti, T., Xu, M., Galanti, L. & Poggio, T. Norm-based Generalization Bounds for Sparse Neural Networks. NeurIPS 2023 (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/file/8493e190ff1bbe3837eca821190b61ff-Paper-Conference.pdf>PDF icon NeurIPS-2023-norm-based-generalization-bounds-for-sparse-neural-networks-Paper-Conference.pdf (577.69 KB)
Gaziv, G., Lee, M. J. & DiCarlo, J. J. Robustified ANNs Reveal Wormholes Between Human Category Percepts. arXiv (2023). at <https://arxiv.org/abs/2308.06887>
Galanti, T., Siegel, Z., Gupte, A. & 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)
Galanti, T., Siegel, Z., Gupte, A. & 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)
Gaziv, G., Lee, M. J. & DiCarlo, J. J. Strong and Precise Modulation of Human Percepts via Robustified ANNs. NeurIPS 2023 (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/hash/d00904cebc0d5b69fada8ad33d0f1422-Abstract-Conference.html>
Gershman, S. J. & Cikara, M. Structure learning principles of stereotype change. Psychonomic Bulletin & Review 30, 1273 - 1293 (2023).
Yu, C., Burgess, N., Sahani, M. & Gershman, S. J. Successor-Predecessor Intrinsic Exploration. Advances in Neural Information Processing Systems 36 (NeurIPS 2023) (2023). at <https://proceedings.neurips.cc/paper_files/paper/2023/hash/e6f2b968c4ee8ba260cd7077e39590dd-Abstract-Conference.html>
Vélez, N., Chen, A. M., Burke, T., Cushman, F. A. & Gershman, S. J. Teachers recruit mentalizing regions to represent learners’ beliefs. Proceedings of the National Academy of Sciences 120, (2023).
Tejwani, R. et al. Zero-shot linear combinations of grounded social interactions with Linear Social MDPs. Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI) (2023).
2024
Gan, Y. & Poggio, T. For HyperBFs AGOP is a greedy approximation to gradient descent. (2024).PDF icon CBMM-Memo-148.pdf (1.06 MB)
Ziyin, L., Chuang, I., Galanti, T. & Poggio, T. Formation of Representations in Neural Networks. (2024).PDF icon CBMM-Memo-150.pdf (4.03 MB)
Peters, B. et al. How does the primate brain combine generative and discriminative computations in vision?. arXiv (2024). at <https://arxiv.org/abs/2401.06005>
Gan, Y., Galanti, T., Poggio, T. & Malach, E. On the Power of Decision Trees in Auto-Regressive Language Modeling. (2024).PDF icon CBMM-Memo-149.pdf (2.11 MB)
Gan, Y., Galanti, T., Poggio, T. & Malach, E. On the Power of Decision Trees in Auto-Regressive Language Modeling. (2024).PDF icon CBMM-Memo-149.pdf (2.11 MB)
Liao, Q. et al. Self-Assembly of a Biologically Plausible Learning Circuit. (2024).PDF icon CBMM-Memo-152.pdf (1.84 MB)
Gershman, S. J. What have we learned about artificial intelligence from studying the brain?. Biological Cybernetics 118, 1 - 5 (2024).
2025
Bono, S. et al. The Indoor-Training Effect: unexpected gains from distribution shifts in the transition function. (2025). at <https://arxiv.org/abs/2401.15856>

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