Publication

Found 908 results
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Kuo, Y. - L. et al. Trajectory Prediction with Linguistic Representations. (2022).PDF icon CBMM-Memo-132.pdf (1.15 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. et al. Trajectory Prediction with Linguistic Representations. 2022 IEEE International Conference on Robotics and Automation (ICRA) (2022). doi:10.1109/ICRA46639.2022.9811928
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. 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. Compositional Networks Enable Systematic Generalization for Grounded Language Understanding. (2021).PDF icon CBMM-Memo-129.pdf (1.2 MB)
Kunhardt, O., Deza, A. & Poggio, T. The Effects of Image Distribution and Task on Adversarial Robustness. (2021).PDF icon CBMM_Memo_116.pdf (5.44 MB)
Kulkarni, T., Kohli, P., Tenenbaum, J. B. & Mansinghka, V. Picture: An Imperative Probabilistic Programming Language for Scene Perception. Computer Vision and Pattern Recognition (2015).
Kubilius, J. et al. Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019) (2019).PDF icon 2019-10-28 NeurIPS-camera_ready.pdf (1.88 MB)
Kryven, M., Ullman, T. D., Cowan, W. & Tenenbaum, J. B. Plans or Outcomes: How Do We Attribute Intelligence to Others?. Cognitive Science 45, (2021).
Kryven, M., Niemi, L., Paul, L. & Tenenbaum, J. B. Choosing a Transformative Experience . Cognitive Sciences Society (2019).
Kryven, M., Scholl, B. & Tenenbaum, J. B. Does intuitive inference of physical stability interruptattention?. Cognitive Sciences Society (2019).
Krompaß, D., Nickel, M. & Tresp, V. The Semantic Web – ISWC 2014 8797, 114-129 (Springer International Publishing, 2014).
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)
Kreiman, G. Cognitive Neuroscience V, (2014).
Kreiman, G. Psychology of Learning and Motivation 70, (2019).
Kreiman, G., Rutishauser, U., Cerf, M. & Fried, I. Single neuron studies of the human brain. Probing cognition (2014).
Kreiman, G. Principles of neural coding (2013).
Kreiman, G. A null model for cortical representations with grandmothers galore. Language, Cognition and Neuroscience 274 - 285 (2017). doi:10.1080/23273798.2016.1218033
Kreiman, G. It's a small dimensional world after all. Physics of Life Reviews 29, 96 - 97 (2019).
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. Biological and Computer Vision. (Cambridge University Press, 2021). doi:10.1017/9781108649995
Kreiman, G. People, objects and interactions in movies. (2014).
Kreiman, G. Neural coding: Stimulating cortex to alter visual perception. Current Biology 33, R117 - R118 (2023).
Kreiman, G. Neural Information Processing Systems (NIPS) 2015 Review. (2016).PDF icon Read the Views & Review article by Gabriel Kreiman (443.87 KB)

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