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Filtering by: Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Contributor Li, Fuxin Remove constraint Contributor: Li, Fuxin Language English [eng] Remove constraint Language: English [eng]

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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
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
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
Detecting Anomalies in Object Appearance and Motion Dynamics

Creator:

Alotaibi, Mazen

Abstract:

Machine common sense remains a broad, potentially unbounded problem in AI. Our focus is to move toward AI systems that can develop common-sense reasoning similar to humans to detect anomalies. In particular, we study the problem of detecting the violation of expectations when object appearance or motion dynamics change from...

Resource Type:

Capstone Project
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...

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Dissertation

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Academic Affiliation

Electrical Engineering and Computer Science[remove]5

Advisor

Li, Fuxin3
Wong, Weng Keen2
Fern, Xiaoli Z.1

Commencement Year

Commencement Year range begin – Commencement Year range end

Current results range from 2020 to 2023

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Committee Member

Fern, Alan4
Lee, Stefan2
Tyler, Brett2
Fu, Xiao1
Jaiswal, Pankaj1
more Committee Members »

Creator

Olson, Matthew L.2
Alotaibi, Mazen1
Khorram, Saeed1
Li, Xingyi1

Contributor

Li, Fuxin[remove]5
Neal, Lawrence2
Qi, Zhongang2
Wong, Weng Keen2
Anirudh, Rushil1
more Contributors »

Date

Date range begin – Date range end

Current results range from 2019 to 2023

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Decade

2020-20294
2010-20191

Degree Field

Computer Science4
Artificial Intelligence2

Degree Level

Master's3
Doctoral2

Degree Name

Master of Science (M.S.)3
Doctor of Philosophy (Ph.D.)2

Language

English [eng][remove]5

License

All rights reserved4

Peer Reviewed

No5

Resource Type

Dissertation2
Masters Thesis2
Capstone Project1

Rights Statement

In Copyright5

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