Index Catalog // ScholarsArchive@OSU

Adversarial Attacks in Natural Language Question-Answering

Creator:: Rahurkar, Prachi S.
Abstract:: Question Answering in natural language processing has achieved significant progress in recent years. Yet, training and testing set methodology to evaluate the language models has proved inadequate. Adversarial examples aid us in finding loopholes inside these models and provide insights into their inner workings. In this work, an evaluation based...
Resource Type:: Masters Thesis
Full Text:: , Jeffrey Heer, and Daniel Weld. Erru- dite: Scalable, reproducible, and testable error analysis. In

New Directions in Search-based Structured Prediction: Multi-Task Learning and Integration of Deep Models

Creator:: Ma, Chao
Abstract:: This thesis studies the problem of structured prediction (SP), where the agent needs to predict a structured output for a given structured input (e.g., Part-of-Speech tagging sequence for an input sentence). Many important applications including machine translation in natural language processing (NLP) and image interpretation in computer vision can be...
Resource Type:: Dissertation

Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms

Creator:: Ibrahim, Shahana
Abstract:: Crowdsourcing is a popular paradigm to address the high demands for labeled data in big data deluge. It aims to produce accurate labels by eﬀectively integrating noisy, non-expert labeling from crowdsourced workers (annotators). The machine learning community has been studying eﬀective crowdsourcing methods for many years, and many models and...
Resource Type:: Masters Thesis

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

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

Understanding the Application and Benefits of Learning-based Methods in Nuclear Science and Engineering

Creator:: Gómez Fernández, Mario Enrique
Abstract:: Known as the fourth industrial revolution, digitization is an ongoing trend in all ﬁelds, in which various industries are integrating information technologies to support and improve their businesses. Nuclear technology industries have also increased their interest in data-driven methods by leveraging the potential of pattern recognition to identify anomalies and...
Resource Type:: Dissertation

Interpretable Machine Learning: Applications in Biology and Genomics

Creator:: Danaee, Padideh
Abstract:: Machine learning (ML) and deep learning (DL) models impact our daily lives with applications in natural language modeling, image analysis, healthcare, genomics, and bioinformatics. The exponential growth of biological sequence data necessitates accompanying advances in computational methods. Although deep learning is highly effective for detecting and classifying biological sequences, challenges...
Resource Type:: Dissertation

Investigating Latent State and Uncertainty Representations in Reinforcement Learning

Creator:: Koul, Anurag
Abstract:: Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...
Resource Type:: Dissertation

Integrating learning and search for structured prediction

Creator:: Doppa, Janardhan Rao
Abstract:: We are witnessing the rise of the data-driven science paradigm, in which massive amounts of data - much of it collected as a side-effect of ordinary human activity - can be analyzed to make sense of the data and to make useful predictions. To fully realize the promise of this...
Resource Type:: Dissertation
Full Text:: , Drew Bagnell, Roni Khardon, Andrew McCallum, Lise Getoor, Jason Eisner, Hal Daumé III, John Langford

Identifying Design Discussion Topics in Open Source Software

Creator:: Chatterjee, Amreeta
Abstract:: When contributing to a software system, developers need to understand the rationale for previous design decisions so that they can adhere to the system’s design. Not doing so can lead to erosion of the overall design quality of the system. However, discussions embedded in a large volume of communication on...
Resource Type:: Masters Thesis

ScholarsArchive@OSU

Adversarial Attacks in Natural Language Question-Answering

New Directions in Search-based Structured Prediction: Multi-Task Learning and Integration of Deep Models

Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms

Efficient training and feature induction in sequential supervised learning

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

Understanding the Application and Benefits of Learning-based Methods in Nuclear Science and Engineering

Interpretable Machine Learning: Applications in Biology and Genomics

Investigating Latent State and Uncertainty Representations in Reinforcement Learning

Integrating learning and search for structured prediction

Identifying Design Discussion Topics in Open Source Software

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