All Publications

2019

A. Adler and Wax, M., “Constant modulus algorithms via low-rank approximation”, Signal Processing, vol. 160, pp. 263 - 270, 2019.
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
CBMM Memo No.
098
H. N. Mhaskar and Poggio, T., “Function approximation by deep networks”. 2019.PDF icon CBMM-Memo-098.pdf (687.36 KB)
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
C. R. Ponce, Xiao, W., Schade, P. F., Hartmann, T. S., Kreiman, G., and Livingstone, M. S., “Evolving Images for Visual Neurons Using a Deep Generative Network Reveals Coding Principles and Neuronal Preferences”, Cell , vol. 177, p. 1009, 2019.PDF icon Author's last draft (20.26 MB)
CBMM Funded
K. Kar, Kubilius, J., Schmidt, K., Issa, E. B., and DiCarlo, J. J., “Evidence that recurrent circuits are critical to the ventral stream’s execution of core object recognition behavior”, Nature Neuroscience, 2019.PDF icon Author's last draft (1.74 MB)
CBMM Funded
O. Lewis and Hermann, K., “Data for free: Fewer-shot algorithm learning with parametricity data augmentation”, in ICLR 2019, 2019.
CBMM Related
M. A. P. Idiart, Villavicencio, A., Katz, B., Rennó-Costa, C., and Lisman, J., “How Does the Brain Represents Language and Answers Questions? Using an AI System to Understand the Underlying Neurobiological Mechanisms”, Frontiers in Computational Neuroscience, vol. 13, 2019.
CBMM Funded
L. Isik, Mynick, A., Pantazis, D., and Kanwisher, N., “The speed of human social interaction perception”, BioRxiv, 2019.
CBMM Funded
E. H. Patzelt, Kool, W., Millner, A. J., and Gershman, S. J., “Incentives Boost Model-Based Control Across a Range of Severity on Several Psychiatric Constructs”, Biological Psychiatry, vol. 85, no. 5, pp. 425 - 433, 2019.
CBMM Funded
S. J. Gershman, “How to never be wrong”, Psychonomic Bulletin & Review, vol. 26, no. 1, pp. 13 - 28, 2019.
CBMM Funded
E. H. Patzelt, Kool, W., Millner, A. J., and Gershman, S. J., “The transdiagnostic structure of mental effort avoidance”, Scientific Reports, vol. 9, no. 1, 2019.
CBMM Related
S. Ullman, “Using neuroscience to develop artificial intelligence”, Science, vol. 363, no. 6428, pp. 692 - 693, 2019.
CBMM Related
S. Ullman, Dorfman, N., and Harari, D., “A model for discovering ‘containment’ relations”, Cognition, vol. 183, pp. 67 - 81, 2019.
CBMM Related
C. I. Calero, Shalom, D. E., Spelke, E. S., and Sigman, M., “Language, gesture, and judgment: Children’s paths to abstract geometry”, Journal of Experimental Child Psychology, vol. 177, pp. 70 - 85, 2019.
CBMM Funded
K. Dobs, Isik, L., Pantazis, D., and Kanwisher, N., “How face perception unfolds over time”, Nature Communications, vol. 10, no. 1, 2019.
CBMM Funded
N. Fazeli, Oller, M., Wu, J., Wu, Z., Tenenbaum, J. B., and Rodriguez, A., “See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion”, Science Robotics, vol. 4, no. 26, p. eaav3123, 2019.
CBMM Funded
A. J. E. Kell and McDermott, J. H., “Deep neural network models of sensory systems: windows onto the role of task constraints”, Current Opinion in Neurobiology, vol. 55, pp. 121 - 132, 2019.
CBMM Related
I. Yildirim, Wu, J., Kanwisher, N., and Tenenbaum, J. B., “An integrative computational architecture for object-driven cortex”, Current Opinion in Neurobiology, vol. 55, pp. 73 - 81, 2019.
CBMM Funded
M. Araya-Polo, Adler, A., Farris, S., and Jennings, J., “Fast and Accurate Seismic Tomography via Deep Learning”, in Deep Learning: Algorithms and Applications, SPRINGER-VERLAG, 2019.
CBMM Related
S. Srivastava, Ben-Yosef, G., and Boix, X., “Minimal images in deep neural networks: Fragile Object Recognition in Natural Images”, in International Conference on Learning Representations (ICLR), New Orleans, La, 2019.
CBMM Funded
W. Xiao, Chen, H., Liao, Q., and Poggio, T., “Biologically-Plausible Learning Algorithms Can Scale to Large Datasets”, in International Conference on Learning Representations, 2019.
CBMM Funded

2018

S. Hu, Ciliberti, D., Grosmark, A. D., Michon, édéric, Ji, D., Penagos, H., áki, örgy, Wilson, M. A., Kloosterman, F., and Chen, Z., “Real-Time Readout of Large-Scale Unsorted Neural Ensemble Place Codes”, Cell Reports, vol. 25, no. 10, pp. 2635 - 2642.e5, 2018.
CBMM Funded
P. Misra, Marconi, A., Peterson, M. F., and Kreiman, G., “Minimal memory for details in real life events”, Scientific Reports, vol. 8, no. 1, 2018.
CBMM Funded
T. D. Ullman, Stuhlmüller, A., Goodman, N. D., and Tenenbaum, J. B., “Learning physical parameters from dynamic scenes.”, Cognitive Psychology, vol. 104, pp. 57-82, 2018.PDF icon T-Ullman-etal_CogPsych_LearningPhysicalParametersFromDynamicScenes.pdf (3.15 MB)
CBMM Funded

Pages