Publication

Found 912 results
Author [ Title(Asc)] Type Year
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Kuo, Y. - L., Barbu, A. & Katz, B. Compositional RL Agents That Follow Language Commands in Temporal Logic. (2021).PDF icon CBMM-Memo-127.pdf (2.12 MB)
Kuo, Y. - L., Katz, B. & Barbu, A. Compositional RL Agents That Follow Language Commands in Temporal Logic. Frontiers in Robotics and AI 8, (2021).PDF icon frobt-08-689550.pdf (1.57 MB)
Kuo, Y. - L., Katz, B. & Barbu, A. Compositional Networks Enable Systematic Generalization for Grounded Language Understanding. (2021).PDF icon CBMM-Memo-129.pdf (1.2 MB)
Barbu, A. et al. The Compositional Nature of Event Representations in the Human Brain. (2014).PDF icon CBMM Memo 011.pdf (3.95 MB)
Schulz, E., Tenenbaum, J. B., Duvenaud, D., Speekenbrink, M. & Gershman, S. J. Compositional inductive biases in function learning. Cogn Psychol 99, 44-79 (2017).
Yu, H., Siddharth, N., Barbu, A. & Siskind, J. Mark. A Compositional Framework for Grounding Language Inference, Generation, and Acquisition in Video. (2015). doi:doi:10.1613/jair.4556
Yuille, A. & Mottaghi, R. Complexity of Representation and Inference in Compositional Models with Part Sharing. (2015).PDF icon CBMM Memo 031.pdf (1.14 MB)
Poggio, T., Liao, Q. & Banburski, A. Complexity Control by Gradient Descent in Deep Networks. Nature Communications 11, (2020).PDF icon s41467-020-14663-9.pdf (431.68 KB)
Hu, J., Zaslavsky, N. & Levy, R. Competition from novel features drives scalar inferences in reference games. Proceedings of the Annual Meeting of the Cognitive Science Society 43, (2021).
Sliwa, J., Marvel, S. R., Ianni, G. A. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Société Francophone de Primatologie (SFDP) Annual Meeting, Paris, France (2018).
Sliwa, J., Marvel, S. R., Ianni, G. A. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Cognitive Neuroscience Society Annual Meeting (CNS), Boston, MA (2018).
Sliwa, J., Marvel, S. R., Ianni, G. A. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Social & Affective Neuroscience Society (SANS) (2018). at <http://www.socialaffectiveneuro.org/conferences.html>
Sliwa, J., Marvel, S. R., Ianni, G. A. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Organization for Computational Neurosciences - CNS 2018 (2018). at <http://www.cnsorg.org/cns-2018>
Sliwa, J., Marvel, S. R. & Freiwald, W. A. Comparing human and monkey neural circuits for processing social scenes. Society for Neuroscience's Annual Meeting - SfN 2017 (2017).
Schulz, E., Quiroga, F. & Gershman, S. J. Communicating Compositional Patterns. Open Mind 4, 25 - 39 (2020).
Baidya, A., Dapello, J., DiCarlo, J. J. & Marques, T. Combining Different V1 Brain Model Variants to Improve Robustness to Image Corruptions in CNNs. NeurIPS 2021 (2021). at <https://nips.cc/Conferences/2021/ScheduleMultitrack?event=41268>
Mendoza-Halliday, D., Schneiderman, M., Kaul, C. & Martinez-Trujillo, J. Combined effects of feature-based working memory and feature-based attention on the perception of visual motion direction. Journal of Vision 11, (2011).
Mendoza-Halliday, D., Schneiderman, M., Kaul, C. & Martinez-Trujillo, J. Combined effects of feature-based working memory and feature-based attention on the perception of visual motion direction. Journal of Vision 11, (2011).
Lafer-Sousa, R., Conway, B. R. & Kanwisher, N. Color-Biased Regions of the Ventral Visual Pathway Lie between Face- and Place-Selective Regions in Humans, as in Macaques. Journal of Neuroscience 36, 1682 - 1697 (2016).
Xiang, Y., Vélez, N. & Gershman, S. J. Collaborative decision making is grounded in representations of other people’s competence and effort. Journal of Experimental Psychology: General 152, 1565 - 1579 (2023).
Zheng, J. et al. Cognitive boundary signals in the human medial temporal lobe shape episodic memory representation. bioRxiv (2021).
Spelke, E. S., Sternberg, R. J., Fiske, S. T. & Foss, D. J. Scientists Making a Difference: One Hundred Eminent Behavioral and Brain Scientists Talk about Their Most Important Contributions (Cambridge University Press, 2016).
Mutch, J., Knoblich, U. & Poggio, T. CNS (“Cortical Network Simulator”): a GPU-based framework for simulating cortically-organized networks. (2010).File cns.tar (1.46 MB)PDF icon MIT-CSAIL-TR-2010-013.pdf (389.38 KB)File (last version before switch to classdef syntax)  (1.05 MB)
Mutch, J. & Turaga, S. cnpkg: 3-D Convolutional Network Package for CNS. (2012).File cnpkg.tar (50 KB)
Kanwisher, N., Gupta, P. & Dobs, K. CNNs reveal the computational implausibility of the expertise hypothesis. iScience 26, 105976 (2023).

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