All Publications

2021

CBMM Memo No.
128
I. Palmer, Rouditchenko, A., Barbu, A., Katz, B., and Glass, J., “Spoken ObjectNet: A Bias-Controlled Spoken Caption Dataset”. 2021.PDF icon CBMM-Memo-128.pdf (2.91 MB)
CBMM Funded
CBMM Memo No.
127
Y. - L. Kuo, Barbu, A., and Katz, B., “Compositional RL Agents That Follow Language Commands in Temporal Logic”. 2021.PDF icon CBMM-Memo-127.pdf (2.12 MB)
CBMM Funded
CBMM Memo No.
126
C. Ross, Barbu, A., and Katz, B., “Measuring Social Biases in Grounded Vision and Language Embeddings”. 2021.PDF icon CBMM-Memo-126.pdf (1.32 MB)
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. Conwell, Mayo, D., Buice, M., Katz, B., Alvarez, G., and Barbu, A., “Large-scale benchmarking of deep neural network models in mouse visual cortex reveals patterns similar to those observed in macaque visual cortex”, Cosyne. 2021.
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
A. Netanyahu, Shu, T., Katz, B., Barbu, A., and Tenenbaum, J. B., “PHASE: PHysically-grounded Abstract Social Events for Machine Social Perception”, in AAAI-21, 2021.
CBMM Funded
C. Ross, Katz, B., and Barbu, A., “Measuring Social Biases in Grounded Vision and Language Embeddings”, NAACL (Annual Conference of the North American Chapter of the Association for Computational Linguistics). 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
L. Isik, Mynick, A., Pantazis, D., and Kanwisher, N., “The speed of human social interaction perception”, NeuroImage, p. 116844, 2020.
CBMM Funded
H. Richardson, Gweon, H., Dodell-Feder, D., Malloy, C., Pelton, H., Keil, B., Kanwisher, N., and Saxe, R., “Response patterns in the developing social brain are organized by social and emotion features and disrupted in children diagnosed with autism spectrum disorder”, Cortex, vol. 125, pp. 12 - 29, 2020.
CBMM Funded
S. Levine, Kleiman-Weiner, M., Schulz, L., Tenenbaum, J. B., and Cushman, F. A., “The logic of universalization guides moral judgment”, Proceedings of the National Academy of Sciences (PNAS), p. 202014505, 2020.
CBMM Funded
T. D. Ullman and Tenenbaum, J. B., “Bayesian Models of Conceptual Development: Learning as Building Models of the World”, Annual Review of Developmental Psychology, vol. 2, no. 1, pp. 533 - 558, 2020.
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
I. Dasgupta, Guo, D., Gershman, S. J., and Goodman, N. D., “Analyzing Machine‐Learned Representations: A Natural Language Case Study”, Cognitive Science, vol. 44, no. 12, 2020.
CBMM Related
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
M. J. McPherson and McDermott, J. H., “Time-dependent discrimination advantages for harmonic sounds suggest efficient coding for memory”, Proceedings of the National Academy of Sciences, vol. 117, no. 50, pp. 32169 - 32180, 2020.
CBMM Related
A. Netanyahu, Shu, T., Katz, B., Barbu, A., and Tenenbaum, J. B., “PHASE: PHysically-grounded Abstract Social Eventsfor Machine Social Perception”, in Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020, 2020.PDF icon phase_physically_grounded_abstract_social_events_for_machine_social_perception.pdf (2.49 MB)
CBMM Funded
M. Nye, Solar-Lezama, A., Tenenbaum, J. B., and Lake, B. M., “Learning Compositional Rules via Neural Program Synthesis”, in Advances in Neural Information Processing Systems 33 pre-proceedings (NeurIPS 2020), 2020.PDF icon 2003.05562.pdf (2.51 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

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