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Filtering by: Creator Schlieder, Tyler D. Contributor Li, Fuxin

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:

. . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 The encoder E, generator G, and discriminator D learn a model of the external environment for
Leveraging Structures of the Data in Deep Learning

Creator:

Li, Xingyi

Abstract:

The performance of deep learning frameworks could be significantly improved through considering the particular underlying structures for each dataset. In this thesis, I summarize our three work about boosting the performance of deep learning models through leveraging structures of the data. In the first work, we theoretically justify that, for...

Resource Type:

Dissertation

Full Text:

, Dr. Prasad Tadepalli, and Dr. Brett Tyler. Thank you for your time and discussions during any exams
Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

Creator:

Khorram, Saeed

Abstract:

In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...

Resource Type:

Masters Thesis

Full Text:

explanation space to be orthogonal; (c) The log penalty function log(1 + q · r2) when q = 1; (d) The log