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  • 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

  • Novelty Detection Under the Multi-Instance Multi-Label Framework

    Creator:
    Lou, Qi
    Abstract:
    Novelty detection plays an important role in machine learning and signal processing. This project studies novelty detection in a new setting where the data object is represented as a bag of instances and associated with multiple class labels, referred to as multi-instance multi-label (MIML) learning. Contrary to the common assumption...
    Resource Type:
    Capstone Project
    Full Text:
    at least one instance xin ∈ Xi belonging to this class. We consider the scenario where an instance

  • A statistical inference framework for finding recurring patterns in large data with applications to energy management

    Creator:
    You, Zeyu
    Abstract:
    We consider the problem of finding unknown patterns that are recurring across multiple sets. For example, finding multiple objects that are present in multiple images or a short DNA code that is repeated across multiple DNA sequences. We first consider a simple problem of finding a single unknown pattern in...
    Resource Type:
    Masters Thesis
    Full Text:
    members, Evgenia Chunikhina, Anh Pham, Sumanth Avadhani and Xin Li, for their support on my thesis

  • Exploration of regression models for cancer noncoding mutation recurrence

    Creator:
    Xu, Tanjin
    Abstract:
    An important impact of the genome technology revolution will be the elucidation of mechanisms of cancer pathogenesis, leading to improvements in the diagnosis of cancer and the selection of cancer treatment. Integrated with current well-studied massive knowledge and findings about the role of protein-coding mutations in cancer, demystifying the functional...
    Resource Type:
    Masters Thesis
    Full Text:
    , especially thanks to Dr. Jichen Yang, Jun He, Xin Liu, Yao Yao and Zheng Liu for putting up with me

  • 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

  • Advances in Simultaneous Translation

    Creator:
    Zheng, Renjie
    Abstract:
    Simultaneous translation, which translates concurrently with the source language speech, is widely used in many scenarios including multilateral organizations. However, it is well known to be one of the most challenging tasks for humans due to the simultaneous perception and production in two languages. On the other hand, simultaneous translation...
    Resource Type:
    Dissertation
    Full Text:
    , Liang Zhang, He Zhang and Sizhen Li. Last but not the least, I would like to thank my family: my wife

  • Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome

    Creator:
    Yao, Yao
    Abstract:
    We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of...
    Resource Type:
    Dissertation

  • Information-theoretic Approach to Design and Evaluate Privacy-preserving and Fair Frameworks for Continuous High-dimensional Data

    Creator:
    Alsulaimawi, Zahir Ahmed Hussein
    Abstract:
    Deep learning is becoming the latest trend in sensitive applications, such as healthcare, criminal justice, and finance. As these new applications emerge, adversaries are circumventing them. Further, there have been concerns about the possibility of bias and discrimination in predictive applications. In order to address these issues, we propose an...
    Resource Type:
    Dissertation
    Full Text:
    Bose, Dr. Weng-Keen Wong, Dr. Fuxin Li, and Dr. Xiao Fu for their fantastic courses. I would like to

  • 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

  • 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