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Filtering by: Creator Wang, Chao-huan Remove constraint Creator: Wang, Chao-huan Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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  • Using Change Representations to Analyze Properties of Variational Software

    Creator:
    Wang, Xiangyu
    Abstract:
    Appropriate representations of variational software simplify the analysis of their properties.This thesis proposes tailored representations of two kinds variational softwares: difference files of merge commits in Git and feature models. For the former, we use the Choice Edit Model, which is based on the choice calculus, to represent changes introduced...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Xiangyu Wang for the degree of

  • 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

  • Visual object discovery and understanding

    Creator:
    Yuan, Jialin
    Abstract:
    Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but...
    Resource Type:
    Dissertation
    Full Text:
    time and effort during the exams and meetings. I extend a special and sincere thanks to Prof. Chao

  • 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
    Full Text:
    , Medisa Danaee, Walker Orr, and Chao Ma. Especially, I am truly appreciative of my dear friend Hamed

  • 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
    Full Text:
    friends Reza Ghaeini, Rasha Obeidat, Chao Ma, Walker Orr, and Charilaos Kanatsoulis. Especially my dear

  • Action Segmentation in Videos: Deep Models, Prediction Diffusion, and Deep-Temporal Augmentations

    Creator:
    Aziere, Nicolas
    Abstract:
    This paper addresses the high model complexity and overconfident frame labeling of state-of-the-art (SOTA) action segmenters. Their complexity is typically justified by the need to sequentially refine action segmentation through multiple stages of a deep architecture. However, this multistage refinement does not take into account uncertainty of frame labeling predicted...
    Resource Type:
    Dissertation
    Full Text:
    , Tieqiao Wang, Ajay Kr- ishna, Khoi Nguyen and Jun Li. Your perspectives and shared enthusiasm have

  • 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

  • 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

  • Predicting Aerodynamic Loads on Highly Flexible Membrane Wings

    Creator:
    Carpenter, Trenton James
    Abstract:
    Through passive adaptation to incidental flow, flexible aerodynamic surfaces exploit effects of increased lift, delayed stall and disturbance rejection. Wings of birds, bats, and insects exhibit these passive effects, and at the same time through the use of structural state feedback sensed from the loads on the wing, active control...
    Resource Type:
    Dissertation

  • Object Tracking-by-Segmentation in Videos

    Creator:
    Chen, Sheng
    Abstract:
    This thesis focuses on the problem of object tracking. Given a video, the general objective of tracking is to track the location over time of one or more targets in the image sequence. This is a very challenging task as algorithms need to deal with problems such as appearance variations,...
    Resource Type:
    Dissertation