All Publications

2020

CBMM Memo No.
104
S. Shalev-Shwartz and Shashua, A., “Can we Contain Covid-19 without Locking-down the Economy?”. 2020.PDF icon CBMM-Memo-104.pdf (425.12 KB)PDF icon CBMM Memo 104 v2 (Mar. 28, 2020) (427.39 KB)PDF icon CBMM Memo 104 v3 (Apr. 1, 2020) (452.94 KB)
CBMM Memo No.
103
T. A. Poggio, “Stable Foundations for Learning: a foundational framework for learning theory in both the classical and modern regime.”. 2020.PDF icon CBMM-Memo-103.pdf (584.54 KB)
CBMM Funded
I. Yildirim, Belledonne, M., Freiwald, W., and Tenenbaum, J., “Efficient inverse graphics in biological face processing”, Science Advances, vol. 6, no. 10, p. eaax5979, 2020.PDF icon eaax5979.full_.pdf (3.22 MB)
CBMM Funded
T. A. 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
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
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
G. Kreiman and Serre, T., “Beyond the feedforward sweep: feedback computations in the visual cortex”, The Year in Cognitive Neuroscience, 2020.PDF icon gk7812.pdf (1.93 MB)
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
S. Liu, “Nature and origins of intuitive psychology in human infants”, 2020.
CBMM Funded

2019

J. A. Leonard, Garcia, A., and Schulz, L. E., “How Adults’ Actions, Outcomes, and Testimony Affect Preschoolers’ Persistence”, Child Development, 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 Edits, adding bound for test error, stability (845.63 KB)PDF icon Added words and a footnote (887.9 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)
CBMM Funded
A. Barbu, Mayo, D., Alverio, J., Luo, W., Wang, C., Gutfreund, D., Tenenabum, 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. Ellis, Nye, M., Pu, Y., Sosa, F., Tenenabum, J. B., and Solar-Lezama, A., “Write, Execute, Assess: Program Synthesis with a REPL”, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon 9116-write-execute-assess-program-synthesis-with-a-repl.pdf (3.9 MB)
CBMM Funded
J. Feather, Durango, A., Gonzalez, R., and McDermott, J. H., “Metamers of neural networks reveal divergence from human perceptual systems”, NIPS 2019. Vancouver, Canada, 2019.PDF icon Feather_etal_2019_NeurIPS_metamers.pdf (4.7 MB)
CBMM Funded
C. Han, Mao, J., Gan, C., Tenenabum, J. B., and Wu, J., “Visual Concept-Metaconcept Learning”, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon 8745-visual-concept-metaconcept-learning.pdf (1.92 MB)
CBMM Funded
N. Muecke, Neu, G., and Rosasco, L., “Beating SGD Saturation with Tail-Averaging and Minibatching”, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon 9422-beating-sgd-saturation-with-tail-averaging-and-minibatching.pdf (389.35 KB)
CBMM Funded
N. Pagliana and Rosasco, L., “Implicit Regularization of Accelerated Methods in Hilbert Spaces”, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon 9591-implicit-regularization-of-accelerated-methods-in-hilbert-spaces.pdf (451.14 KB)
CBMM Funded
K. A. Smith, Mei, L., Yao, S., Wu, J., Spelke, E. S., Tenenabum, J. B., and Ullman, T. D., “Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations”, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon ADEPT_NeurIPS.pdf (11.07 MB)
CBMM Funded
C. Stephenson, Feather, J., Padhy, S., Elibol, O., Tang, H., McDermott, J. H., and Chung, S. Y., “Untangling in Invariant Speech Recognition”, Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon 9583-untangling-in-invariant-speech-recognition.pdf (2.09 MB)
CBMM Funded
J. Kubilius, Schrimpf, M., Kar, K., Rajalingham, R., Hong, H., Majaj, N. J., Issa, E. B., Bashivan, P., Prescott-Roy, J., Schmidt, K., Nayebi, A., Bear, D., Yamins, D. L. K., and DiCarlo, J. J., “Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs”, 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada, 2019.PDF icon 2019-10-28 NeurIPS-camera_ready.pdf (1.88 MB)
CBMM Funded
N. Jacoby, Undurraga, E. A., McPherson, M. J., Valdés, J., Ossandón, T., and McDermott, J. H., “Universal and Non-universal Features of Musical Pitch Perception Revealed by Singing”, Current Biology, 2019.
CBMM Funded
CBMM Memo No.
101
H. Sanders, Wilson, M. A., and Gershman, S. J., “Hippocampal Remapping as Hidden State Inference”. 2019.PDF icon CBMM-Memo-101.pdf (12.78 MB)
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

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