All Publications

2023

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, vol. 6, 2023.PDF icon research.0024.pdf (5.19 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
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

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
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
CBMM Memo No.
134
T. Galanti and Poggio, T., “SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks”. 2022.PDF icon Implicit Rank Regularization.pdf (1.42 MB)
CBMM Funded
T. Galanti and Galanti, L., “On the Implicit Bias Towards Minimal Depth of Deep Neural Networks”, arXiv, 2022.PDF icon 2202.09028.pdf (2 MB)
CBMM Funded
F. S. Kamps, Richardson, H., N. Murty, A. Ratan, Kanwisher, N., and Saxe, R., “Using child‐friendly movie stimuli to study the development of face, place, and object regions from age 3 to 12 years”, Human Brain Mapping, 2022.
CBMM Related
CBMM Memo No.
121
M. Yamada, D'Amario, V., Takemoto, K., Boix, X., and Sasaki, T., “Transformer Module Networks for Systematic Generalization in Visual Question Answering”. 2022.PDF icon CBMM-Memo-121.pdf (1.06 MB)PDF icon version 2 (3/22/2023) (1.33 MB)
CBMM Funded
S. Madan, Henry, T., Dozier, J., Ho, H., Bhandari, N., Sasaki, T., Durand, F., Pfister, H., and Boix, X., “When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations”, Nature Machine Intelligence, vol. 4, no. 2, pp. 146 - 153, 2022.
CBMM Funded
CBMM Memo No.
119
A. Sakai, Sunagawa, T., Madan, S., Suzuki, K., Katoh, T., Kobashi, H., Pfister, H., Sinha, P., Boix, X., and Sasaki, T., “Three approaches to facilitate DNN generalization to objects in out-of-distribution orientations and illuminations”. 2022.PDF icon CBMM-Memo-119.pdf (31.08 MB)
CBMM Funded
CBMM Memo No.
135
A. Gupte, Banburski, A., and Poggio, T., “PCA as a defense against some adversaries”. 2022.PDF icon CBMM-Memo-135.pdf (2.58 MB)
CBMM Funded
W. Berrios and Deza, A., “Joint rotational invariance and adversarial training of a dual-stream Transformer yields state of the art Brain-Score for Area V4”, in BrainScore Workshop at COSYNE, 2022.
CBMM Funded
A. Harrington and Deza, A., “Finding Biological Plausibility for Adversarially Robust Features via Metameric Tasks”, International Conference on Learning Representations (ICLR). 2022.
CBMM Funded

2021

Y. Xiang, Graeber, T., Enke, B., and Gershman, S. J., “Confidence and central tendency in perceptual judgment”, Attention, Perception, & Psychophysics, vol. 83, no. 7, pp. 3024 - 3034, 2021.
CBMM Funded
A. Baidya, Dapello, J., DiCarlo, J. J., and Marques, T., “Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs”, in NeurIPS 2021, 2021.
CBMM Funded
J. Gant, Banburski, A., Deza, A., and Poggio, T., “Evaluating the Adversarial Robustness of a Foveated Texture Transform Module in a CNN”, NeurIPS 2021. 2021.
CBMM Funded
A. Ratan N. Murty, Bashivan, P., Abate, A., DiCarlo, J. J., and Kanwisher, N., “Computational models of category-selective brain regions enable high-throughput tests of selectivity”, Nature Communications, vol. 12, no. 1, 2021.PDF icon s41467-021-25409-6.pdf (6.47 MB)
CBMM Funded

Pages