All Publications

2024

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

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 The_Janus_effects_of_SGD_vs_GD__high_noise_and_low_rank-4.pdf (2.83 MB)PDF icon Updated with appendix showing empirically that the main results extend to deep nonlinear networks (2.95 MB)
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
T. Poggio and Magrini, M., Cervelli menti algoritmi. Sperling & Kupfer, 2023, p. 272.
CBMM Funded
CBMM Memo No.
143
Y. Gan and Poggio, T. A., “A Homogeneous Transformer Architecture”. 2023.PDF icon CBMM-Memo-143.pdf (1.07 MB)
CBMM Funded
Y. Han, Poggio, T. A., and Cheung, B., “System Identification of Neural Systems: If We Got It Right, Would We Know?”, Proceedings of the 40th International Conference on Machine Learning, PMLR, vol. 202. pp. 12430-12444, 2023.PDF icon han23d.pdf (797.48 KB)
CBMM Funded
CBMM Memo No.
142
Y. Xie, Li, Y., and Rangamani, A., “Skip Connections Increase the Capacity of Associative Memories in Variable Binding Mechanisms”. 2023.PDF icon CBMM-Memo-142.pdf (1.64 MB)
CBMM Funded
CBMM Memo No.
141
A. Rangamani, Lindegaard, M., Galanti, T., and Poggio, T., “Feature learning in deep classifiers through Intermediate Neural Collapse”. 2023.PDF icon Feature_Learning_memo.pdf (2.16 MB)
CBMM Funded
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
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
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
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

E. M. Meyers, “NeuroDecodeR: A package for neural decoding analyses in R”, bioRxiv, 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
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
F. Anselmi and Poggio, T., “Representation Learning in Sensory Cortex: a theory”, IEEE Access, pp. 1 - 1, 2022.PDF icon Representation_Learning_in_Sensory_Cortex_a_theory.pdf (1.17 MB)
CBMM Funded
CBMM Memo No.
137
A. Rangamani and Xie, Y., “Understanding the Role of Recurrent Connections in Assembly Calculus”. 2022.PDF icon CBMM-Memo-137.pdf (1.49 MB)
CBMM Funded
CBMM Memo No.
136
Y. Han, Poggio, T., and Cheung, B., “System identification of neural systems: If we got it right, would we know?”. 2022.PDF icon CBMM-Memo-136.pdf (1.75 MB)
CBMM Funded
J. Dapello, Kar, K., Schrimpf, M., Geary, R., Ferguson, M., Cox, D. D., and DiCarlo, J. J., “Aligning Model and Macaque Inferior Temporal Cortex Representations Improves Model-to-Human Behavioral Alignment and Adversarial Robustness”, bioRxiv, 2022.
CBMM Related
C. Guo, Lee, M. J., Leclerc, G., Dapello, J., Rao, Y., Madry, A., and DiCarlo, J. J., “Adversarially trained neural representations may already be as robust as corresponding biological neural representations”, arXiv, 2022.
CBMM Related
K. Kar, “A computational probe into the behavioral and neural markers of atypical facial emotion processing in autism”, The Journal of Neuroscience, pp. JN-RM-2229-21, 2022.
CBMM Related
A. Banburski and Rangamani, A., “Neural Collapse in Deep Homogeneous Classifiers and the role of Weight Decay”, in IEEE International Conference on Acoustics, Speech and Signal Processing, Singapore, 2022.
CBMM Funded
K. Dobs, Martinez-Trujillo, J., Kell, A. J. E., and Kanwisher, N., “Brain-like functional specialization emerges spontaneously in deep neural networks”, Science Advances, vol. 8, no. 11, 2022.
CBMM Funded

Pages