Index Catalog // ScholarsArchive@OSU

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

Learning with Limited Labeled Data in Natural Language Processing

Creator:: Obeidat, Rasha
Abstract:: The advent of deep learning models leads to a substantial improvement in a wide range of NLP tasks, achieving state-of-art performances without any hand-crafted features. However, training deep models requires a massive amount of labeled data. Labeling new data as a new task or domain emerges consumes time and efforts...
Resource Type:: Dissertation

Learning with Partial Supervision for Clustering and Classification

Creator:: Pei, Yuanli
Abstract:: In the field of machine learning, clustering and classification are two fundamental tasks. Traditionally, clustering is an unsupervised method, where no supervision about the data is available for learning; classification is a supervised task, where fully-labeled data are collected for training a classifier. In some scenarios, however, we may not...
Resource Type:: Dissertation

Simulator-Defined MDP Planning with Applications in Natural Resource Management

Creator:: Alkaee Taleghan, Majid
Abstract:: This work is inspired by problems in natural resource management centered on the challenge of invasive species. Computing optimal management policies for maintaining ecosystem sustainable is challenging. Many ecosystem management problems can be formulated as MDP (Markov Decision Process) planning problems. In a simulator-defined MDP, the Markovian dynamics and rewards...
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

Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework

Creator:: Duong, Thai
Abstract:: We investigate the data collection problem in sensor networks. The network consists of a number of stationary sensors deployed at different sites for sensing and storing data locally. A mobile element moves from sites to sites to collect data from the sensors periodically. There are different costs associated with the...
Resource Type:: Masters Thesis
Full Text:: 5000 0.8 0.82 0.84 0.86 0.88 0.9 0.92 0.94 0.96 0.98 1 Number of algorithm steps S LE M

An Information Modeling Framework for Support of Sustainable Manufacturing System Design Decision Making

Creator:: Shankar Raman, Arvind
Abstract:: Manufacturing technology has continuously evolved and advanced over the past century; this has led to an increase in the production of consumer and industrial goods driven by simultaneous growth in population and wealth. Despite the resulting economic and labor growth, environmental impacts of manufacturing have increased dramatically due to the...
Resource Type:: Dissertation

Learning and Improving Policies for Probabilistic Planning Problems

Creator:: Issakkimuthu, Murugeswari
Abstract:: In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...
Resource Type:: Dissertation

Knowledge Base Completion Leveraging Natural Language Data

Creator:: Shahbazi, Hamed
Abstract:: Information about named entities (real-world objects) is usually harvested from different sources and organized as a multiple relational directed graph in Knowledge Bases (KBs). KBs play essential roles in many NLP modules including question answering, fact-checking, search engines, etc. KBs are big but still incomplete: relational information among entities is...
Resource Type:: Dissertation

ScholarsArchive@OSU

Investigating Latent State and Uncertainty Representations in Reinforcement Learning

Learning with Limited Labeled Data in Natural Language Processing

Learning with Partial Supervision for Clustering and Classification

Simulator-Defined MDP Planning with Applications in Natural Resource Management

Improving and Understanding Deep Models for Natural Language Comprehension

Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework

An Information Modeling Framework for Support of Sustainable Manufacturing System Design Decision Making

Learning and Improving Policies for Probabilistic Planning Problems

Knowledge Base Completion Leveraging Natural Language Data

Limit your search

Academic Affiliation

Advisor

Commencement Year

Committee Member

Creator

Date

Decade

Degree Field

Degree Level

Degree Name

Language

License

Non-Academic Affiliation

Peer Reviewed

Resource Type

Rights Statement

Subject

Search Constraints

Search Results

Limit your search