Publication

Found 360 results
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2019
Yildirim, I., Wu, J., Kanwisher, N. & Tenenbaum, J. B. An integrative computational architecture for object-driven cortex. Current Opinion in Neurobiology 55, 73 - 81 (2019).
Kell, A. J. E. & McDermott, J. H. Invariance to background noise as a signature of non-primary auditory cortex. Nature Communications 10, (2019).
Schwettmann, S., Tenenbaum, J. B. & Kanwisher, N. Invariant representations of mass in the human brain. eLife 8, (2019).
Kreiman, G. It's a small dimensional world after all. Physics of Life Reviews 29, 96 - 97 (2019).
Ross, C., Berzak, Y., Katz, B. & Barbu, A. Learning Language from Vision. Workshop on Visually Grounded Interaction and Language (ViGIL) at the Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS) (2019).
Bashivan, P., Kar, K. & DiCarlo, J. J. Neural Population Control via Deep Image Synthesis. Science 364, (2019).PDF icon Author's last draft (18.45 MB)
Barbu, A. et al. ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models. Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 9142-objectnet-a-large-scale-bias-controlled-dataset-for-pushing-the-limits-of-object-recognition-models.pdf (16.31 MB)
Brewer, K., Mittman, B., Kominsky, J. & Henes, J. Open Source Subject Database Project (OSSDP). (2019).
Cohen, M. A. et al. Representational similarity precedes category selectivity in the developing ventral visual pathway. NeuroImage 197, 565 - 574 (2019).
Cohen, M. A. et al. Representational similarity precedes category selectivity in the developing ventral visual pathway. NeuroImage 197, 565 - 574 (2019).
Cohen, M. A. et al. Representational similarity precedes category selectivity in the developing ventral visual pathway. NeuroImage 197, 565 - 574 (2019).
Cohen, M. A. et al. Representational similarity precedes category selectivity in the developing ventral visual pathway. NeuroImage 197, 565 - 574 (2019).
Jozwik, K. M., Schrimpf, M., Kanwisher, N. & DiCarlo, J. J. To find better neural network models of human vision, find better neural network models of primate vision. BioRxiv (2019). at <https://www.biorxiv.org/content/10.1101/688390v1.full>
Patzelt, E. H., Kool, W., Millner, A. J. & Gershman, S. J. The transdiagnostic structure of mental effort avoidance. Scientific Reports 9, (2019).
Kreiman, G. Psychology of Learning and Motivation 70, (2019).
2020
Kim, D. et al. The ability to predict actions of others from distributed cues is still developing in children. PsyArXiv Preprints (2020). doi:10.31234/osf.io/pu3tfPDF icon Action_prediction_in_children.pdf (427.84 KB)
Kreiman, G. & Serre, T. Beyond the feedforward sweep: feedback computations in the visual cortex. Annals of the New York Academy of Sciences 1464, 222 - 241 (2020).
Kreiman, G. & Serre, T. Beyond the feedforward sweep: feedback computations in the visual cortex. Ann. N.Y. Acad. Sci. | Special Issue: The Year in Cognitive Neuroscience 1464, 222-241 (2020).PDF icon gk7812.pdf (1.93 MB)
Jacquot, V., Ying, J. & Kreiman, G. Can Deep Learning Recognize Subtle Human Activities?. CVPR 2020 (2020).
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)
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)
Kuo, Y. - L., Katz, B. & Barbu, A. Deep compositional robotic planners that follow natural language commands . International Conference on Robotics and Automation (ICRA) (2020).
Kuo, Y. - L., Katz, B. & Barbu, A. Deep compositional robotic planners that follow natural language commands . International Conference on Robotics and Automation (ICRA) (2020).
Kuo, Y. - L., Katz, B. & Barbu, A. Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. (2020).PDF icon CBMM-Memo-125.pdf (2.12 MB)
Kuo, Y. - L., Katz, B. & Barbu, A. Encoding formulas as deep networks: Reinforcement learning for zero-shot execution of LTL formulas. (2020).PDF icon CBMM-Memo-125.pdf (2.12 MB)

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