All Publications

2020

T. Poggio, Liao, Q., and Banburski, A., “Complexity Control by Gradient Descent in Deep Networks”, Nature Communications, vol. 11, 2020.PDF icon s41467-020-14663-9.pdf (431.68 KB)
CBMM Funded
M. Schrimpf, Sato, F., Sanghavi, S., and DiCarlo, J. J., “Temporal information for action recognition only needs to be integrated at a choice level in neural networks and primates ”, in COSYNE, Denver, CO, USA, 2020.
CBMM Funded
Y. Han, Roig, G., Geiger, G., and Poggio, T., “Scale and translation-invariance for novel objects in human vision”, Scientific Reports, vol. 10, no. 1411, 2020.PDF icon s41598-019-57261-6.pdf (1.46 MB)
CBMM Funded
J. M. Hicks and McDermott, J. H., “Segregation from Noise as Outlier Detection ”, in Association for Research in Otolaryngology, San Jose, CA, USA, 2020.
CBMM Funded
V. Jacquot, Ying, J., and Kreiman, G., “Can Deep Learning Recognize Subtle Human Activities?”, CVPR 2020, 2020.
CBMM Funded
D. Kim, McMahon, E., Mehr, S., Nakayama, K., Spelke, E. S., and Vaziri-Pashkam, M., “The ability to predict actions of others from distributed cues is still developing in children”, PsyArXiv Preprints, 2020.PDF icon Action_prediction_in_children.pdf (427.84 KB)
CBMM Funded
H. Mhaskar and Poggio, T., “An analysis of training and generalization errors in shallow and deep networks”, Neural Networks, vol. 121, pp. 229 - 241, 2020.
CBMM Funded
A. J. Thomas, Saxe, R., and Spelke, E. S., “Infants represent 'like-kin' affiliation ”, in Budapest Conference on Cognitive Development, Budapest, Hungary, 2020.
CBMM Funded
M. Zhang, Tseng, C., and Kreiman, G., “Putting visual object recognition in context”, CVPR 2020, 2020.PDF icon gk7876.pdf (3.12 MB)
CBMM Funded
CBMM Memo No.
125
Y. - L. Kuo, Katz, B., and Barbu, A., “Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas”. 2020.PDF icon CBMM-Memo-125.pdf (2.12 MB)
CBMM Funded
CBMM Memo No.
124
Y. - L. Kuo, Katz, B., and Barbu, A., “Deep compositional robotic planners that follow natural language commands”. 2020.PDF icon CBMM-Memo-124.pdf (1.03 MB)
CBMM Funded
Y. - L. Kuo, Katz, B., and Barbu, A., “Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas”, in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2020.
CBMM Funded
S. Liu, “Nature and origins of intuitive psychology in human infants”, 2020.
CBMM Funded
K. A. Smith, Mei, L., Yao, S., Wu, J., Spelke, E. S., Tenenbaum, J. B., and Ullman, T. D., “The fine structure of surprise in intuitive physics: when, why, and how much?”, in Proceedings of the 42th Annual Meeting of the Cognitive Science Society - Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020, virtual, July 29 - August 1, 2020, 2020.
CBMM Funded

2019

J. A. Leonard, Garcia, A., and Schulz, L., “How Adults’ Actions, Outcomes, and Testimony Affect Preschoolers’ Persistence”, Child Development, 2019.
CBMM Funded
S. Schwettmann, Tenenbaum, J. B., and Kanwisher, N., “Invariant representations of mass in the human brain”, eLife, vol. 8, 2019.
CBMM Funded
M. A. Cohen, Dilks, D. D., Koldewyn, K., Weigelt, S., Feather, J., Kell, A. J. E., Keil, B., Fischl, B., Zöllei, L., Wald, L., Saxe, R., and Kanwisher, N., “Representational similarity precedes category selectivity in the developing ventral visual pathway”, NeuroImage, vol. 197, pp. 565 - 574, 2019.
CBMM Funded
M. F. Peterson, Zaun, I., Hoke, H., Jiahui, G., Duchaine, B., and Kanwisher, N., “Eye movements and retinotopic tuning in developmental prosopagnosia”, Journal of Vision, vol. 19, no. 9, p. 7, 2019.
CBMM Funded
K. M. Jozwik, Lee, M., Marques, T., Schrimpf, M., and Bashivan, P., “Large-scale hyperparameter search for predicting human brain responses in the Algonauts challenge”, in The Algonauts Project: Explaining the Human Visual Brain Workshop 2019 , MIT, Cambridge MA, 2019.
CBMM Funded
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
Y. - L. Kuo, Katz, B., and Barbu, A., “Deep Compositional Robotic Planners that Follow Natural Language Commands.”, Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS). Vancouver Convention Centre, Vancouver, Canada, 2019.
CBMM Funded
W. Młynarski and McDermott, J. H., “Ecological origins of perceptual grouping principles in the auditory system”, Proceedings of the National Academy of Sciences, vol. 116, no. 50, pp. 25355 - 25364, 2019.
CBMM Funded
C. Ross, Berzak, Y., Katz, B., and Barbu, A., “Learning Language from Vision.”, in Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), Vancouver Convention Center, Vancouver, Canada, 2019.
CBMM Funded
A. Barbu, Mayo, D., Alverio, J., Luo, W., Wang, C., Gutfreund, D., Tenenbaum, J. B., and Katz, B., “ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models”, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon 9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf (16.31 MB)
CBMM Funded
K. Brewer, Mittman, B., Kominsky, J., and Henes, J., “Open Source Subject Database Project (OSSDP)”. Center for Brains, Minds and Machines, 2019.
CBMM Funded

Pages