All Publications

2022

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
CBMM Memo No.
120
S. Ullman, Assif, L., Strugatski, A., Vatashsky, B. - Z., Levi, H., Netanyahu, A., and Yaari, A. Uri, “Image interpretation by iterative bottom-up top- down processing”. 2021.PDF icon CBMM-Memo-120.pdf (2.83 MB)
CBMM Funded
CBMM Memo No.
118
T. Poggio, “From Marr’s Vision to the Problem of Human Intelligence”. 2021.PDF icon CBMM-Memo-118.pdf (362.19 KB)
CBMM Funded
K. Kar, Schrimpf, M., Schmidt, K., and DiCarlo, J. J., “ Chemogenetic suppression of macaque V4 neurons produces retinotopically specific deficits in downstream IT neural activity patterns and core object recognition behavior”, Journal of Vision, vol. 21, 2021.
CBMM Related
T. Marques, Schrimpf, M., and DiCarlo, J. J., “Multi-scale hierarchical neural network models that bridge from single neurons in the primate primary visual cortex to object recognition behavior”, bioRxiv, 2021.PDF icon 2021.03.01.433495v2.full_.pdf (3.23 MB)
CBMM Funded
S. M. Landi, Viswanathan, P., Serene, S., and Freiwald, W. A., “A fast link between face perception and memory in the temporal pole”, Science, p. eabi6671, 2021.
CBMM Funded
Y. Du, Smith, K. A., Ullman, T., Tenenbaum, J. B., and Wu, J., “Unsupervised Discovery of 3D Physical Objects”, in International Conference on Learning Representations, 2021.
CBMM Related
Z. Yang and Freiwald, W. A., “Joint encoding of facial identity, orientation, gaze, and expression in the middle dorsal face areaSignificance”, Proceedings of the National Academy of Sciences, vol. 118, no. 33, 2021.
CBMM Related
A. Adler, Araya-Polo, M., and Poggio, T., “Deep Learning for Seismic Inverse Problems: Toward the Acceleration of Geophysical Analysis Workflows”, IEEE Signal Processing Magazine, vol. 38, no. 2, pp. 89 - 119, 2021.
CBMM Funded
J. Traer, Norman-Haignere, S., and McDermott, J. H., “Causal inference in environmental sound recognition”, Cognition, 2021.
CBMM Funded
CBMM Memo No.
116
O. Kunhardt, Deza, A., and Poggio, T., “The Effects of Image Distribution and Task on Adversarial Robustness”. 2021.PDF icon CBMM_Memo_116.pdf (5.44 MB)
CBMM Funded
CBMM Memo No.
114
T. Poggio, “From Associative Memories to Powerful Machines”. 2021.PDF icon v1.0 (1.01 MB)PDF icon v1.3Section added August 6 on self attention (3.9 MB)
CBMM Funded
W. A. Freiwald and Hosoya, H., “Neuroscience: A Face’s Journey through Space and Time”, Current Biology, vol. 31, no. 1, pp. R13 - R15, 2021.
CBMM Funded
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, vol. 109, no. 1, pp. 164 - 176.e5, 2021.
CBMM Funded
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 v1.0 (4.61 MB)PDF icon v1.4corrections to generalization section (5.85 MB)PDF icon v1.7Small edits (22.65 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

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

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