All Publications

2021

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

T. Poggio, Banburski, A., and Liao, Q., “Theoretical issues in deep networks”, Proceedings of the National Academy of Sciences, p. 201907369, 2020.PDF icon PNASlast.pdf (915.3 KB)
CBMM Funded
J. Dapello, Marques, T., Schrimpf, M., Geiger, F., Cox, D., and DiCarlo, J. J., “Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations”, Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020). 2020.
CBMM Funded
W. A. Freiwald, “Gross means Great”, Progress in Neurobiology, vol. 195, p. 101924, 2020.
CBMM Related
W. A. Freiwald, “Social interaction networks in the primate brain”, Current Opinion in Neurobiology, vol. 65, pp. 49 - 58, 2020.
CBMM Funded
E. Malkin, Deza, A., and Poggio, T., “CUDA-Optimized real-time rendering of a Foveated Visual System”, in Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020, 2020.PDF icon Foveated_Drone_SVRHM_2020.pdf (13.44 MB)PDF icon v1 (12/15/2020) (14.7 MB)
CBMM Funded
T. Poggio and Liao, Q., “Explicit regularization and implicit bias in deep network classifiers trained with the square loss”, arXiv, 2020.
CBMM Funded
D. J. Schaeffer, Selvanayagam, J., Johnston, K. D., Menon, R. S., Freiwald, W. A., and Everling, S., “Face selective patches in marmoset frontal cortexAbstract”, Nature Communications, vol. 11, no. 1, 2020.
CBMM Related
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
M. Schrimpf, Kubilius, J., Lee, M. J., N. Murty, A. Ratan, Ajemian, R., and DiCarlo, J. J., “Integrative Benchmarking to Advance Neurally Mechanistic Models of Human Intelligence”, Neuron, vol. 108, no. 3, pp. 413 - 423, 2020.
CBMM Related
K. Kar and DiCarlo, J. J., “Fast Recurrent Processing via Ventrolateral Prefrontal Cortex Is Needed by the Primate Ventral Stream for Robust Core Visual Object Recognition”, Neuron, 2020.PDF icon PIIS0896627320307595.pdf (3.92 MB)
CBMM Funded
T. Poggio and Banburski, A., “An Overview of Some Issues in the Theory of Deep Networks”, IEEJ Transactions on Electrical and Electronic Engineering, vol. 15, no. 11, pp. 1560 - 1571, 2020.
CBMM Funded
J. Bill, Pailian, H., Gershman, S. J., and Drugowitsch, J., “Hierarchical structure is employed by humans during visual motion perception”, Proceedings of the National Academy of Sciences, vol. 117, no. 39, pp. 24581 - 24589, 2020.
CBMM Related
CBMM Memo No.
112
T. Poggio, Liao, Q., and Xu, M., “Implicit dynamic regularization in deep networks”. 2020.PDF icon v1.2 (2.29 MB)PDF icon v.59 Update on rank (2.43 MB)
CBMM Funded
H. Mhaskar and Poggio, T., “Function approximation by deep networks”, Communications on Pure & Applied Analysis, vol. 19, no. 8, pp. 4085 - 4095, 2020.PDF icon 1534-0392_2020_8_4085.pdf (514.57 KB)
CBMM Funded
R. Rajalingham, Kar, K., Sanghavi, S., Dehaene, S., and DiCarlo, J. J., “The inferior temporal cortex is a potential cortical precursor of orthographic processing in untrained monkeys”, Nature Communications, vol. 11, no. 1, 2020.PDF icon s41467-020-17714-3.pdf (25.01 MB)
CBMM Funded
CBMM Memo No.
111
S. Madan, Henry, T., Dozier, J., Ho, H., Bhandari, N., Sasaki, T., Durand, F., Pfister, H., and Boix, X., “On the Capability of Neural Networks to Generalize to Unseen Category-Pose Combinations”. 2020.PDF icon CBMM-Memo-111.pdf (9.76 MB)
CBMM Funded
CBMM Memo No.
107
T. Poggio and Cooper, Y., “Loss landscape: SGD has a better view”. 2020.PDF icon CBMM-Memo-107.pdf (1.03 MB)PDF icon Typos and small edits, ver11 (955.08 KB)PDF icon Small edits, corrected Hessian for spurious case (337.19 KB)
CBMM Funded
CBMM Memo No.
110
M. Vuyyuru Reddy, Banburski, A., Pant, N., and Poggio, T., “Biologically Inspired Mechanisms for Adversarial Robustness”. 2020.PDF icon CBMM_Memo_110.pdf (3.14 MB)
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
CBMM Memo No.
108
A. Rangamani, Rosasco, L., and Poggio, T., “For interpolating kernel machines, the minimum norm ERM solution is the most stable”. 2020.PDF icon CBMM_Memo_108.pdf (1015.14 KB)PDF icon Better bound (without inequalities!) (1.03 MB)
CBMM Funded
CBMM Memo No.
105
K. M. Villalobos, Štih, V., Ahmadinejad, A., Sundaram, S., Dozier, J., Francl, A., Azevedo, F., Sasaki, T., and Boix, X., “Do Neural Networks for Segmentation Understand Insideness?”. 2020.PDF icon CBMM-Memo-105.pdf (4.63 MB)PDF icon CBMM Memo 105 v2 (July 2, 2020) (3.2 MB)PDF icon CBMM Memo 105 v3 (January 25, 2022) (8.33 MB)
CBMM Funded
W. Lotter, Kreiman, G., and Cox, D., “A neural network trained for prediction mimics diverse features of biological neurons and perception”, Nature Machine Intelligence, vol. 2, no. 4, pp. 210 - 219, 2020.
CBMM Funded
CBMM Memo No.
103
T. Poggio, “Stable Foundations for Learning: a framework for learning theory (in both the classical and modern regime).”. 2020.PDF icon Original file (584.54 KB)PDF icon Corrected typos and details of "equivalence" CV stability and expected error for interpolating machines. Added Appendix on SGD.  (905.29 KB)PDF icon Edited Appendix on SGD. (909.19 KB)PDF icon Deleted Appendix. Corrected typos etc (880.27 KB)PDF icon Added result about square loss and min norm (898.03 KB)
CBMM Funded

Pages