rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Deep Generative Models: Pitfalls and Fixes
  name:
    name part: Armandpour, Mohammadreza
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Huang, Jianhua
    role:
      role term: thesis advisor.
  name:
    name part: Pati, Debdeep
    role:
      role term: thesis advisor.
  name:
    name part: Texas A&M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: The promise of deep learning is to discover rich, hierarchical models that represent probability distributions over the kinds of data encountered in artificial intelligence applications, such as natural images, audio waveforms containing speech, and symbols in natural language corpora. In recent years, the most striking successes in deep learning have involved generative models. However, in their vanilla forms, generative models have a number of shortcomings and failure modes that can hinder their application: they can be difficult to train on high dimensional data, and they can fail in tasks such as the generation of realistic artificial data. In this thesis, we first explore the reasons for these failures in the adversarial-based generative models and propose a novel approach to alleviate these shortfalls. Then, we discuss how a learned generative model can be employed for a downstream task such as speech recognition. The electronic version of this dissertation is accessible from https://hdl.handle.net/1969.1/197220
  note: by Mohammadreza Armandpour.
  note: "Major Subject: Statistics"
  note: Includes vita.
  note: Doctor of Philosophy Texas A&M University 2022 https://hdl.handle.net/1969.1/197220
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Statistics
  subject:
    topic: GANs
  subject:
    topic: Generative Models
  subject:
    topic: Deep Learning
  subject:
    topic: ASR
  subject:
    topic: Speech recognition
  identifier: https://hdl.handle.net/1969.1/197220
  record info:
    record content source: TXA
    record creation date: 240819
    record change date: 20240820145854.2
    record identifier: in00005463527
    language of cataloging:
      language term: eng