Publication

Found 912 results
Author [ Title(Asc)] Type Year
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D
Kuo, Y. - L., Katz, B. & 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), (2019). at <https://vigilworkshop.github.io/>
Kuo, Y. - L., Katz, B. & Barbu, A. Deep compositional robotic planners that follow natural language commands. (2020).PDF icon CBMM-Memo-124.pdf (1.03 MB)
Mao, J. et al. Deep Captioning with Multimodal Recurrent Neural Networks (m-RNN). (2015).PDF icon CBMM Memo 033.pdf (839.42 KB)
Sliwa, J. & Freiwald, W. A. A Dedicated Network for Social Interaction Processing in the Primate Brain. Science Vol. 356, pp. 745-749 (2017).
Madhavan, R. et al. Decrease in gamma-band activity tracks sequence learning. Frontiers in Systems Neuroscience 8, (2015).PDF icon fnsys-08-00222.pdf (5.62 MB)
Kliemann, D., Jacoby, N., Anzellottti, S. & Saxe, R. Decoding task and stimulus representations in face-responsive cortex. Cognitive Neuropsychology (2016).
Kliemann, D., Jacoby, N., Anzellottti, S. & Saxe, R. Decoding task and stimulus representation in face-responsive cortex. (2015).
Pramod, R. T., Mieczkowski, E., Fang, C. X., Tenenbaum, J. B. & Kanwisher, N. Decoding predicted future states from the brain’s “physics engine”. Science Advances 11, (2025).
Zhang, Y. et al. Decoding of human identity by computer vision and neuronal vision. Scientific Reports 13, (2023).PDF icon s41598-022-26946-w.pdf (1.88 MB)
Zhang, Y. et al. Decoding of human identity by computer vision and neuronal visionAbstract. Scientific Reports 13, (2023).
Chen, Z. & Wilson, M. A. Deciphering neural codes of memory during sleep. Trends in Neurosciences (2017).PDF icon proof (2.98 MB)
Meyers, E. A Data Science approach to analyzing neural data. Joint Statistical Meetings (2017).
Lewis, O. & Hermann, K. Data for free: Fewer-shot algorithm learning with parametricity data augmentation. ICLR 2019 (2019).
Rutishauser, U., Cerf, M. & Kreiman, G. Single Neuron Studies of the Brain: Probing Cognition (2014).
Liu, S. et al. Dangerous Ground: One-Year-Old Infants are Sensitive to Peril in Other Agents’ Action PlansAbstract. Open Mind 6, 211 - 231 (2022).
C
Malkin, E., Deza, A. & Poggio, T. CUDA-Optimized real-time rendering of a Foveated Visual System. Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop at NeurIPS 2020 (2020). at <https://arxiv.org/abs/2012.08655>PDF icon Foveated_Drone_SVRHM_2020.pdf (13.44 MB)PDF icon v1 (12/15/2020) (14.7 MB)
Xiao, Y. et al. Cross-task specificity and within-task invariance of cognitive control processes. Cell Reports 42, 111919 (2023).PDF icon PIIS2211124722018174.pdf (3.97 MB)
Ullman, T., Tenenbaum, J. B. & Spelke, E. S. Critical Cues in Early Physical Reasoning. SRCD (2017).
Lin, H. & Tegmark, M. Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language. arXiv.org (2016).PDF icon Critical Behavior from Deep Dynamics: A Hidden Dimension in Natural Language (1.64 MB)
Spokes, A. C. & Spelke, E. S. The cradle of social knowledge: Infants' reasoning about caregiving and affiliation. Cognition 159, 102-116 (2017).
Kool, W., Gershman, S. J. & Cushman, F. A. Cost-Benefit Arbitration Between Multiple Reinforcement-Learning Systems. Psychol Sci 28, 1321-1333 (2017).
Nassi, J. J., Gomez-Laberge, C., Kreiman, G. & Born, R. T. Corticocortical feedback increases the spatial extent of normalization. Frontiers in Systems Neuroscience 8, 105 (2014).
Livingstone, M. S., Arcaro, M. J. & Schade, P. F. Cortex Is Cortex: Ubiquitous Principles Drive Face-Domain Development. Trends in Cognitive Sciences (2018). doi:10.1016/j.tics.2018.10.009PDF icon 1-s2.0-S1364661318302572-main.pdf (260.4 KB)
Spelke, E. S., Barner, D. & Baron, A. S. Core Knowledge and Conceptual Change (Oxford University Press, 2016).
Dillon, M. R. & Spelke, E. S. Core geometry in perspective. Developmental Science (2014). doi:10.1111/desc.12266

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