Index Catalog // ScholarsArchive@OSU

Automatic Explanations of Computation Results with Value Decompositions and Dominating Sets

Creator:: Kumar, Prashant
Abstract:: In an increasingly computation-driven world, algorithms and mathematical models significantly impact decision making across various fields. To foster trust and understanding, it is crucial to provide users with clear and concise explanations of the reasoning behind the results produced by computational tools, especially when recommendations appear counterintuitive. Legal frameworks in...
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

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

Active Learning from Examples, Queries and Explanations

Creator:: Hamidi-Haines, Mandana
Abstract:: Humans are remarkably efficient in learning by interacting with other people and observing their behavior. Children learn by watching their parents’ actions and mimic their behavior. When they are not sure about their parents demonstration, they communicate with them, ask questions, and learn from their feedback. On the other hand,...
Resource Type:: Dissertation

Open Set Learning with Counterfactual Images

Creator:: Neal, Lawrence W.
Abstract:: In open set recognition, a classifier must label instances of known classes while detecting instances of unknown classes not encountered during training. To detect unknown classes while still generalizing to new instances of existing classes, this thesis introduces a dataset augmentation technique called counterfactual image generation. This approach, based on...
Resource Type:: Masters Thesis

Bayesian optimization with empirical constraints

Creator:: Azimi, Javad
Abstract:: Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...
Resource Type:: Dissertation

Probabilistic models for quality control in environmental sensor networks

Creator:: Dereszynski, Ethan W.
Abstract:: Networks of distributed, remote sensors are providing ecological scientists with a view of our environment that is unprecedented in detail. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures in network communications. This behavior manifests as corrupt or missing measurements in the...
Resource Type:: Dissertation

Hierarchical structure discovery and transfer in sequential decision problems

Creator:: Mehta, Neville
Abstract:: Acting intelligently to efficiently solve sequential decision problems requires the ability to extract hierarchical structure from the underlying domain dynamics, exploit it for optimal or near-optimal decision-making, and transfer it to related problems instead of solving every problem in isolation. This dissertation makes three contributions toward this goal. The first...
Resource Type:: Dissertation

Efficient training and feature induction in sequential supervised learning

Creator:: Hao, Guohua
Abstract:: Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...
Resource Type:: Dissertation

Effective decision-theoretic assistance through relational hierarchical models

Creator:: Natarajan, Sriraam
Abstract:: Building intelligent computer assistants has been a long-cherished goal of AI. Many intelligent assistant systems were built and fine-tuned to specific application domains. In this work, we develop a general model of assistance that combines three powerful ideas: decision theory, hierarchical task models and probabilistic relational languages. We use the...
Resource Type:: Dissertation

ScholarsArchive@OSU

Automatic Explanations of Computation Results with Value Decompositions and Dominating Sets

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

Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

Active Learning from Examples, Queries and Explanations

Open Set Learning with Counterfactual Images

Bayesian optimization with empirical constraints

Probabilistic models for quality control in environmental sensor networks

Hierarchical structure discovery and transfer in sequential decision problems

Efficient training and feature induction in sequential supervised learning

Effective decision-theoretic assistance through relational hierarchical models

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