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Filtering by: Creator Wu, JunJie Remove constraint Creator: Wu, JunJie Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Degree Field Computer Science Remove constraint Degree Field: Computer Science

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Infant Hand Detection and Tracking

Creator:

Mishra, Shridhar

Abstract:

Hand detection is a fundamental step for many hand-related computer vision tasks, such as gesture recognition, hand pose estimation, hand sign language translation, and so on. However, robustly detecting hands is a challenging task because of drastic changes in appearance based on finger articulation and changes in lighting conditions, camera...

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
Source-Routed Multicast Schemes for Large-Scale Cloud Data Center Networks

Creator:

Alqahtani, Jarallah Ali m

Abstract:

Data centers (DCs) have been witnessing unprecedented growth in size, number and complexity in recent years. They consist of tens of thousands of servers interconnected by fast network switches, hosting and enabling numerous applications with various traffic characteristics and requirements. As a result, DC networks have been presented with several...

Resource Type:

Dissertation
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
Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

Creator:

Khorram, Saeed

Abstract:

The advancement of artificial intelligence (AI) has led to transformative developments across multiple sectors, fostering innovation and redefining our interactions with technology. As AI matures and becomes integrated into society, it offers numerous opportunities to address global challenges and revolutionize a wide array of human endeavors. These advances are driven...

Resource Type:

Dissertation
Stake-Free Evaluations of Black-Box Optimization and Spatio-Temporal Graph Network Algorithms

Creator:

Merrill, Erich, III

Abstract:

Papers proposing novel machine learning algorithms tend to present the algorithm or technique in question in the best possible light. The standard practice is generally for authors to emphasize their proposed algorithms' performance in the precise setting where it is maximally impressive, often by only fully evaluating their best known...

Resource Type:

Dissertation
Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

Creator:

Dodge, Jonathan E.

Abstract:

Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...

Resource Type:

Dissertation
Improving and Understanding Deep Models for Natural Language Comprehension

Creator:

Ghaeini, Reza

Abstract:

Natural Language Comprehension is a challenging domain of Natural Language Processing. To improve a model’s language comprehension/understanding, one approach would be to enrich the structure of the model to enhance its capability in learning the latent rules of the language. In this dissertation, we will ﬁrst introduce several deep models...

Resource Type:

Dissertation

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Academic Affiliation

Electrical Engineering and Computer Science[remove]8

Advisor

Li, Fuxin2
Burnett, Margaret M.1
Dietterich, Thomas G.1
Fern, Alan1
Fern, Xiaoli Z.1
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Commencement Year

Commencement Year range begin – Commencement Year range end

Current results range from 2020 to 2023

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Committee Member

Fern, Alan3
Tadepalli, Prasad3
Todorovic, Sinisa2
Wong, Weng-Keen2
Fern, Xiaoli1
more Committee Members »

Creator

Khorram, Saeed2
Alqahtani, Jarallah Ali M1
Dodge, Jonathan E.1
Ghaeini, Reza1
Merrill, Erich, Iii1
more Creators »

Contributor

Li, Fuxin2
Anirudh, Rushil1
Bremer, Peer Timo1
Dixit, Gaurav1
Dolatina, Nima1
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Date

Date range begin – Date range end

Current results range from 2020 to 2023

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Decade

2020-20298

Degree Field

Computer Science[remove]8
Artificial Intelligence1

Degree Level

Doctoral6
Master's2

Degree Name

Doctor of Philosophy (Ph.D.)6
Master of Science (M.S.)2

Language

English [eng]8

License

All rights reserved8

Peer Reviewed

No8

Resource Type

Dissertation6
Masters Thesis2

Rights Statement

In Copyright8

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