All Publications

2022

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
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

CBMM Memo No.
117
M. Xu, Rangamani, A., Banburski, A., Liao, Q., Galanti, T., and Poggio, T., “Dynamics and Neural Collapse in Deep Classifiers trained with the Square Loss”. 2021.PDF icon JMLR__2021-22.pdf (4.61 MB)PDF icon Fixed Appendix (13.31 MB)PDF icon Several updates, new figures, low rank bias, bounds (8.87 MB)
CBMM Funded
CBMM Memo No.
115
A. Banburski, De La Torre, F., Pant, N., Shastri, I., and Poggio, T., “Distribution of Classification Margins: Are All Data Equal?”. 2021.PDF icon CBMM Memo 115.pdf (9.56 MB)PDF icon arXiv version (23.05 MB)
CBMM Funded
C. Li and Deza, A., “What Matters In Branch Specialization? Using a Toy Task to Make Predictions”, in Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop at NeurIPS, 2021.
CBMM Funded
B. Wang, Mayo, D., Deza, A., Barbu, A., and Conwell, C., “On the use of Cortical Magnification and Saccades as Biological Proxies for Data Augmentation”, in Shared Visual Representations in Human and Machine Intelligence (SVRHM) Workshop at NeurIPS, 2021.
CBMM Funded

2020

CBMM Memo No.
113
A. Banburski, Gandhi, A., Alford, S., Dandekar, S., Chin, P., and Poggio, T., “Dreaming with ARC”, Learning Meets Combinatorial Algorithms workshop at NeurIPS 2020. 2020.PDF icon CBMM Memo 113.pdf (1019.64 KB)
CBMM Funded
CBMM Memo No.
109
A. Deza, Liao, Q., Banburski, A., and Poggio, T., “Hierarchically Local Tasks and Deep Convolutional Networks”. 2020.PDF icon CBMM_Memo_109.pdf (2.12 MB)
CBMM Funded

2019

CBMM Memo No.
102
T. Poggio, Kur, G., and Banburski, A., “Double descent in the condition number”. 2019.PDF icon Fixing typos, clarifying error in y, best approach is crossvalidation (837.18 KB)PDF icon Incorporated footnote in text plus other edits (854.05 KB)PDF icon Deleted previous discussion on kernel regression and deep nets: it will appear, extended, in a separate paper (795.28 KB)PDF icon correcting a bad typo (261.24 KB)PDF icon Deleted plot of condition number of kernel matrix: we cannot get a double descent curve  (769.32 KB)
CBMM Funded
CBMM Memo No.
100
T. Poggio, Banburski, A., and Liao, Q., “Theoretical Issues in Deep Networks”. 2019.PDF icon CBMM Memo 100 v1 (1.71 MB)PDF icon CBMM Memo 100 v3 (8/25/2019) (1.31 MB)PDF icon CBMM Memo 100 v4 (11/19/2019) (1008.23 KB)
CBMM Funded
M. Kryven, Scholl, B., and Tenenbaum, J. B., “ Does intuitive inference of physical stability interruptattention?”, Cognitive Sciences Society. 2019.
CBMM Funded
S. J. Gershman, “How to never be wrong”, Psychonomic Bulletin & Review, vol. 26, no. 1, pp. 13 - 28, 2019.
CBMM Funded
M. Araya-Polo, Adler, A., Farris, S., and Jennings, J., “Fast and Accurate Seismic Tomography via Deep Learning”, in Deep Learning: Algorithms and Applications, SPRINGER-VERLAG, 2019.
CBMM Related

2018

T. D. Ullman, Stuhlmüller, A., Goodman, N. D., and Tenenbaum, J. B., “Learning physical parameters from dynamic scenes.”, Cognitive Psychology, vol. 104, pp. 57-82, 2018.PDF icon T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB)
CBMM Funded
CBMM Memo No.
095
K. M. Villalobos, Dozier, J., Stih, V., Francl, A., Azevedo, F., Poggio, T., Sasaki, T., and Boix, X., “Can Deep Neural Networks Do Image Segmentation by Understanding Insideness?”. 2018.PDF icon CBMM-Memo-095.pdf (1.96 MB)
CBMM Funded
A. Tacchetti, Voinea, S., and Evangelopoulos, G., “Trading robust representations for sample complexity through self-supervised visual experience”, in Advances in Neural Information Processing Systems 31, Montreal, Canada, 2018, pp. 9640–9650.PDF icon trading-robust-representations-for-sample-complexity-through-self-supervised-visual-experience.pdf (3.32 MB)PDF icon NeurIPS2018_Poster.pdf (6.12 MB)
CBMM Memo No.
093
L. Arend, Han, Y., Schrimpf, M., Bashivan, P., Kar, K., Poggio, T., DiCarlo, J. J., and Boix, X., “Single units in a deep neural network functionally correspond with neurons in the brain: preliminary results”. 2018.PDF icon CBMM-Memo-093.pdf (2.99 MB)
CBMM Funded
N. O'Brien, Latessa, S., Evangelopoulos, G., and Boix, X., “The Language of Fake News: Opening the Black-Box of Deep Learning Based Detectors”, in workshop on "AI for Social Good", NIPS 2018, Montreal, Canada, 2018.PDF icon fake-news-paper-NIPS.pdf (147.36 KB)PDF icon fake-news-paper-NIPS_2018_v2.pdf (147.36 KB)
CBMM Funded
A. Tacchetti, Isik, L., and Poggio, T., “Invariant Recognition Shapes Neural Representations of Visual Input”, Annual Review of Vision Science, vol. 4, no. 1, pp. 403 - 422, 2018.PDF icon annurev-vision-091517-034103.pdf (1.55 MB)
CBMM Funded
T. Gerstenberg, Ullman, T. D., Nagel, J., Kleiman-Weiner, M., Lagnado, D. A., and Tenenbaum, J. B., “Lucky or clever? From changed expectations to attributions of responsibility”, Cognition, 2018.
CBMM Funded
CBMM Memo No.
091
Q. Liao, Miranda, B., Hidary, J., and Poggio, T., “Classical generalization bounds are surprisingly tight for Deep Networks”. 2018.PDF icon CBMM-Memo-091.pdf (1.43 MB)PDF icon CBMM-Memo-091-v2.pdf (1.88 MB)
CBMM Funded
CBMM Memo No.
090
A. Banburski, Liao, Q., Miranda, B., Poggio, T., Rosasco, L., Hidary, J., and De La Torre, F., “Theory III: Dynamics and Generalization in Deep Networks”. 2018.PDF icon Original, intermediate versions are available under request (2.67 MB)PDF icon CBMM Memo 90 v12.pdf (4.74 MB)PDF icon Theory_III_ver44.pdf Update Hessian (4.12 MB)PDF icon Theory_III_ver48 (Updated discussion of convergence to max margin) (2.56 MB)PDF icon fixing errors and sharpening some proofs (2.45 MB)
CBMM Funded
CBMM Memo No.
084
Z. Zhang, Qiao, S., Xie, C., Shen, W., Wang, B., and Yuille, A., “Single-Shot Object Detection with Enriched Semantics”. 2018.PDF icon CBMM-Memo-084.pdf (1.92 MB)
CBMM Funded

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