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Filtering by: Creator Zégre, Nicolas P. Remove constraint Creator: Zégre, Nicolas P. Contributor Li, Fuxin Remove constraint Contributor: Li, Fuxin

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  • Counterfactual States for Atari Agents via Generative Deep Learning

    Creator:
    Olson, Matthew L.
    Abstract:
    Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past work has mainly focused on explaining why an action was chosen in a given state. A different type of explanation that is useful is...
    Resource Type:
    Masters Thesis
    Full Text:
    ))), whereH(p) = − ∑ i pilog(pi) Therefore, the adversarial loss can be written as: LAdv = λ |X | ∑ (s,a

  • Characterizing and Addressing Dataset Shift with Deep Learning

    Creator:
    Olson, Matthew L.
    Abstract:
    This dissertation delves into understanding, characterizing, and addressing dataset shift in deep learning, a pervasive issue for deployed machine learning systems. Integral aspects of the problem are examined: We start with the use of counterfactual explanations in order to characterize the behavior of deep reinforcement learning agents in visual input...
    Resource Type:
    Dissertation
    Full Text:
    highlighted in bold. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 3.3 A table of p-values for a