All Publications

2017

N. Dehghani, “Theoretical principles of multiscale spatiotemporal control of neuronal networks: a complex systems perspective”, 2017.PDF icon StimComplexity.pdf (218.1 KB)
CBMM Related
N. Dehghani, “Design of the Artificial: lessons from the biological roots of general intelligence”, 2017.PDF icon DesignArtificial_Dehghani_arXiv.pdf (222.47 KB)
CBMM Related
G. A del Molino, Boix, X., Lim, J., and Tan, A., “Active Video Summarization: Customized Summaries via On-line Interaction.”, in AAAI Conference on Artificial Intelligence, 2017.PDF icon 21-Garcia-del-Molino-14856.pdf (413.77 KB)
CBMM Related
M. R. Dillon and Spelke, E. S., “Spatial cognition across development”, in SRCD, Austin, TX, 2017.
CBMM Funded
M. R. Dillon and Spelke, E. S., “Young children's use of distance and angle information during map reading”, SRCD. Austin, TX, 2017.
CBMM Related
S. J. Gershman and Daw, N. D., “Reinforcement learning and episodic memory in humans and animals: an integrative framework”, Annual Review of Psychology, vol. 68, 2017.PDF icon GershmanDaw17.pdf (422.11 KB)
CBMM Funded
T. Gerstenberg, Peterson, M. F., Goodman, N. D., Lagnado, D. A., and Tenenbaum, J. B., “Eye-Tracking Causality”, Psychological Science, 2017.PDF icon eye_tracking_causality.pdf (8.04 MB)
CBMM Funded
N. Golowich, Rakhlin, A., and Shamir, O., “Size-Independent Sample Complexity of Neural Networks”, 2017.PDF icon 1712.06541.pdf (278.77 KB)
CBMM Related
Y. Han, Roig, G., Geiger, G., and Poggio, T., “On the Human Visual System Invariance to Translation and Scale”, Vision Sciences Society. 2017.
CBMM Funded
Y. Han, Roig, G., Geiger, G., and Poggio, T., “Is the Human Visual System Invariant to Translation and Scale?”, in AAAI Spring Symposium Series, Science of Intelligence, 2017.
CBMM Funded
S. Dae Houlihan and Saxe, R., “Modeling emotion attributions as inference in an intuitive theory of mind.”, Mechanisms Underlying Emotion Regulation and Developmental Psychopathology. University of Wisconsin - Madison, 2017.
CBMM Funded
L. Isik, Singer, J., Madsen, J., Kanwisher, N., and Kreiman, G., “What is changing when: Decoding visual information in movies from human intracranial recordings”, Neuroimage, 2017.
CBMM Funded
W. Kool, Gershman, S. J., and Cushman, F. A., “Thinking fast or slow? A reinforcement-learning approach”, Society for Personality and Social Psychology. San Antonio, TX, 2017.PDF icon KoolEtAl_SPSP_2017.pdf (670.35 KB)
CBMM Funded
C. Liu, Mao, J., Sha, F., and Yuille, A., “Attention Correctness in Neural Image Captioning”, in AAAI 2017, 2017.PDF icon 1605.09553.pdf (2.22 MB)
CBMM Funded
S. Liu, Ullman, T., Tenenbaum, J. B., and Spelke, E. S., “Ten-month-old infants infer value from effort”, SRCD. Austin, TX, 2017.
CBMM Funded
S. Liu, Ullman, T., Tenenbaum, J. B., and Spelke, E. S., “Ten-month-old infants infer value from effort”, Society for Research in Child Development. 2017.
CBMM Funded
W. Lotter, Kreiman, G., and Cox, D., “Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning”, in ICLR, 2017.PDF icon 1605.08104.pdf (2.9 MB)
CBMM Funded
E. Meyers, “A Data Science approach to analyzing neural data”, in Joint Statistical Meetings, 2017.
CBMM Funded
H. Mhaskar, Liao, Q., and Poggio, T., “When and Why Are Deep Networks Better Than Shallow Ones?”, AAAI-17: Thirty-First AAAI Conference on Artificial Intelligence. 2017.
CBMM Funded
W. Mlynarski and McDermott, J. H., “Learning Mid-Level Codes for Natural Sounds”, Association for Otolaryngology Mid-Winter Meeting. 2017.
CBMM Funded
W. Mlynarski and McDermott, J. H., “Lossy Compression of Uninformative Stimuli in the Auditory System”, Association for Otolaryngology Mid-Winter Meeting. 2017.
CBMM Funded
W. Mlynarski and McDermott, J. H., “Adaptive Compression of Statistically Homogenous Sensory Signals”, Computational and Systems Neuroscience (COSYNE). 2017.
CBMM Funded
J. Mutch, Anselmi, F., Tacchetti, A., Rosasco, L., Leibo, J. Z., and Poggio, T., “Invariant Recognition Predicts Tuning of Neurons in Sensory Cortex”, in Computational and Cognitive Neuroscience of Vision, Springer, 2017, pp. 85-104.
CBMM Funded
G. Roig, Chen, F., Boix, X., and Poggio, T., “Eccentricity Dependent Deep Neural Networks for Modeling Human Vision”, Vision Sciences Society. 2017.
CBMM Funded
J. Sliwa and Freiwald, W. A., “From agents to actions to interactions: Uncovering multiple social networks in the primate brain”, Society for Social and Affective Neuroscience. 2017.
CBMM Related

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