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Robert Fergus

Associate Professor of Computer Science

    20012019
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    Fingerprint Weighted list of dominant concepts in the researcher's publications (titles and abstracts).

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    Research Output 2001 2019

    Hierarchical RL using an ensemble of proprioceptive periodic policies

    Marino, K., Gupta, A., Szlam, A. & Fergus, R., Jan 1 2019.

    Research output: Contribution to conferencePaper

    reward
    Navigation
    Ensemble
    Reward
    Values

    Composable planning with attributes

    Zhang, A., Lerer, A., Sukhbaatar, S., Fergus, R. & Szlam, A., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), Vol. 13. p. 9292-9307 16 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Planning

    Intrinsic motivation and automatic curricula via asymmetric self-play

    Sukhbaatar, S., Lin, Z., Kostrikov, I., Synnaeve, G., Szlam, A. & Fergus, R., Jan 1 2018.

    Research output: Contribution to conferencePaper

    intrinsic motivation
    Curricula
    curriculum
    reward
    Curriculum

    Learning by Asking Questions

    Misra, I., Girshick, R., Fergus, R., Hebert, M., Gupta, A. & Van Der Maaten, L., Dec 14 2018, Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2018. IEEE Computer Society, p. 11-20 10 p. 8578107. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Curricula
    Learning systems
    Testing

    Modeling others using oneself in multi-agent reinforcement learning

    Raileanu, R., Denton, E., Szlam, A. & Fergus, R., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Krause, A. & Dy, J. (eds.). International Machine Learning Society (IMLS), Vol. 10. p. 6779-6788 10 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Reinforcement learning