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Filtering by: Creator Hong, Soon Koo Remove constraint Creator: Hong, Soon Koo Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science

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  • Poisoning machine learning models to increase membership inference risks

    Creator:
    Le, Hoang
    Abstract:
    Deep learning is now being utilized widely in applications where sensitive data is being used for model training, for example, in health care. In this scenario, any data leakage will cause privacy concerns to whose data records are used to train the model. An attacker can actively cause privacy leakage...
    Resource Type:
    Masters Thesis
    Full Text:
    Models to Increase Membership Inference Risks Abstract approved: Sanghyun Hong Deep learning is now

  • Deep Learning for Human and Biological Languages

    Creator:
    Hong, Junki
    Abstract:
    We explore the application of deep learning to the disparate fields of natural language processing and computational biology. Both the sentences uttered by humans as well as the RNA and protein sequences found within the cells of their bodies can be considered formal languages in computer science, as sets of...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF Juneki Hong for the

  • Dimensionality Reduction Techniques for Compositional Data : An Empirical Study

    Creator:
    George, Rogen
    Abstract:
    Compositional data is a type of data where the features are non-negative and always sum to a constant. This type of data is frequently encountered in many fields such as microbiology, geology, economics and natural language processing. Compositional data has unique statistical properties that makes it difficult to apply standard...
    Resource Type:
    Masters Thesis
    Full Text:
    opportunity to work with him and be a part of his lab. I acknowledge the support of Dr.Si Hong Park who

  • Assessing and Finding Faults in AI: Two Empirical Studies

    Creator:
    Khanna, Roli
    Abstract:
    With the advent of Artificial Intelligence (AI) in every sphere of life in today's day and age, it has become increasingly important for non-AI experts to be able to comprehend the underlying logic of how AI systems work, assess them and find faults in these systems, particularly when they are...
    Resource Type:
    Masters Thesis

  • Atomic layer deposition of nanolaminate high-κ gate dielectrics for amorphous-oxide semiconductor thin film transistors

    Creator:
    Triska, Joshua B.
    Abstract:
    Nanolaminate dielectrics combine two or more insulating materials in a many-layered film. These structures can be made to significantly outperform films composed of a single one of their constituent materials by adjusting the composition ratio, arrangement, and size of the component layers. In this work, atomic layer deposition (ALD) is...
    Resource Type:
    Masters Thesis

  • Parsing with Recurrent Neural Networks

    Creator:
    Cross, James Henry, III
    Abstract:
    Machine learning models for natural language processing have traditionally relied on large numbers of discrete features, built up from atomic categories such as word forms and part-of-speech labels, which are considered completely distinct from each other. Recently however, the advent of dense feature representations coupled with deep learning techniques has...
    Resource Type:
    Dissertation
    Full Text:
    Juneki Hong. I am also in eternal debt to my master’s degree advisers Irina Gladkova and Michael

  • Explaining and Improving Neural Machine Translation

    Creator:
    Yang, Yilin
    Abstract:
    Machine Translation, the task of automatically translating between human languages has been studied for decades. This task is used to be solved by count-based statistical models, e.g. Phrase-based Statistical Machine Translation (PBSMT), which solves the translation problem by separately training a statistical language model and a translation model. Recently, Neural...
    Resource Type:
    Dissertation
    Full Text:
    Zheng, Qi Lyu, Jun li, Anurag Koul, Matthew Olson, Juneki Hong, Neale Ratzlaff, Yiren Wang, Jie hao

  • Nontraditional amorphous oxide semiconductor thin-film transistor fabrication

    Creator:
    Sundholm, Eric Steven
    Abstract:
    Fabrication techniques and process integration considerations for amorphous oxide semiconductor (AOS) thin-film transistors (TFTs) constitute the central theme of this dissertation. Within this theme three primary areas of focus are pursued. The first focus involves formulating a general framework for assessing passivation. Avoiding formation of an undesirable backside accumulation layer...
    Resource Type:
    Dissertation
    Full Text:
    Passivation Schemes In 2005 Hong et al. published their results in which they passivated bottom-gate ZTO

  • Towards Direct Simultaneous Speech Translation

    Creator:
    Chen, Junkun
    Abstract:
    Simultaneous speech translation (SimulST) is widely useful in many cross-lingual communication scenarios, including multinational conferences and international traveling. Since text-based simultaneous machine translation (SimulMT) has achieved great success in recent years. The conventional cascaded approach for SimulST uses a pipeline of streaming ASR followed by simultaneous MT but suffers from...
    Resource Type:
    Dissertation
    Full Text:
    Zheng, Juneki Hong, Göksu Öztürk Miraç, Atsuhito Kita, Ning Dai, Liang Zhang, He Zhang, and Sizhen Li

  • Structured Neural Models for Natural Language Processing

    Creator:
    Ma, Mingbo
    Abstract:
    Most tasks in natural language processing (NLP) involves structured information from both input (e.g., a sentence or a paragraph) and output (e.g., a tag sequence, a parse tree or a translated sentence). While neural models achieve great successes in other domains such as computer vision, applying those frameworks to NLP...
    Resource Type:
    Dissertation
    Full Text:
    dependency parsing (Mc- Donald and Pereira, 2006; Koo and Collins, 2010), we also incorporate siblings for a