All Publications

2020

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

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 results on kernel regression and deep nets which will appear in a separate paper (821.98 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
CBMM Memo No.
099
A. Adler, Wax, M., and Pantazis, D., “Brain Signals Localization by Alternating Projections”, arXiv. 2019.PDF icon CBMM-Memo-099.pdf (421.67 KB)
CBMM Funded
S. Liu, Brooks, N. B., and Spelke, E. S., “Origins of the concepts cause, cost, and goal in prereaching infants”, PNAS, 2019.PDF icon Author's last draft (2.58 MB)
CBMM Funded
A. Adler and Wax, M., “Constant modulus algorithms via low-rank approximation”, Signal Processing, vol. 160, pp. 263 - 270, 2019.
CBMM Funded
A. Adler and Wax, M., “Direct Localization by Partly Calibrated Arrays: A Relaxed Maximum Likelihood Solution”, in 27th European Signal Processing Conference, EUSIPCO 2019, A Coruna, Spain, 2019.
CBMM Funded
T. D. Ullman, Kosoy, E., Yildirim, I., Soltani, A. Arsalan, Siegel, M., Tenenabum, J. B., and Spelke, E. S., “Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects”, Cognitive Science Society. Montreal, Canada, 2019.PDF icon Draping an Elephant: Uncovering Children's Reasoning About Cloth-Covered Objects.pdf (2.62 MB)
CBMM Funded
A. Adler, Araya-Polo, M., and Poggio, T., “Deep Recurrent Architectures for Seismic Tomography”, in 81st EAGE Conference and Exhibition 2019, 2019.
CBMM Funded
S. V. Norman-Haignere, Kanwisher, N., McDermott, J. H., and Conway, B. R., “Divergence in the functional organization of human and macaque auditory cortex revealed by fMRI responses to harmonic tones”, Nature Neuroscience, 2019.
CBMM Funded
CBMM Memo No.
098
H. N. Mhaskar and Poggio, T., “An analysis of training and generalization errors in shallow and deep networks”. 2019.PDF icon CBMM-Memo-098.pdf (687.36 KB)PDF icon CBMM Memo 098 v4 (08/2019) (2.63 MB)
CBMM Funded
P. Bashivan, Kar, K., and DiCarlo, J. J., “Neural Population Control via Deep Image Synthesis”, Science, vol. 364, no. 6439, 2019.PDF icon Author's last draft (18.45 MB)
CBMM Funded

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