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Filtering by: Alternative title ssim Technical note no. F-6 Remove constraint Alternative title ssim: Technical note no. F-6 Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan

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  • Action Segmentation with Limited Supervision

    Creator:
    Li, Jun
    Abstract:
    In this dissertation, we address action segmentation in videos under limited supervision. The goal of action segmentation is to predict an action class for each frame of a video. The limited supervision means ground truth labels of video frames are not available in training. We focus on three types of...
    Resource Type:
    Dissertation
    Full Text:
    of actions present; and (3) Unsupervised learning, where no ground truth is available. To address

  • Part-based and Uncertainty-Aware Few-shot Object Segmentation in Images

    Creator:
    Nguyen, Khoi D.
    Abstract:
    This dissertation addresses few-shot object segmentation in images. The goal of segmentation is to label every image pixel with a class of the object occupying that pixel, where the class may represent a semantic object category or instance. In few-shot segmentation, training and test datasets have different classes. Every new...
    Resource Type:
    Dissertation
    Full Text:
    technical contributions include an extension of Mask-RCNN – the state-of-the-art instance segmenter aimed

  • Bayesian optimization with empirical constraints

    Creator:
    Azimi, Javad
    Abstract:
    Bayesian Optimization (BO) methods are often used to optimize an unknown function f(•) that is costly to evaluate. They typically work in an iterative manner. In each iteration, given a set of observation points, BO algorithms select k ≥ 1 points to be evaluated. The results of those points are...
    Resource Type:
    Dissertation
    Full Text:
    noisy observation of f(x). Note that x is a random variable given Q, and we assume this conditional

  • Characterizing and Addressing Dataset Shift with Deep Learning

    Creator:
    Olson, Matthew L.
    Abstract:
    This dissertation delves into understanding, characterizing, and addressing dataset shift in deep learning, a pervasive issue for deployed machine learning systems. Integral aspects of the problem are examined: We start with the use of counterfactual explanations in order to characterize the behavior of deep reinforcement learning agents in visual input...
    Resource Type:
    Dissertation
    Full Text:
    , no matter how complicated. My mother for her excellent writing advice. My daughters for constantly

  • Adaptive Movement Intent Decoding for Intuitive Control of Neuroprostheses

    Creator:
    George, Damon A.
    Abstract:
    Movement intent decoders, which interpret volitional movement intent from human bioelectric signals, can be incorporated into modern neuroprostheses to offer people living with limb loss or paralysis the potential to regain their lost motor control. Machine learning methods have become the research standard for continuous decoders with high degrees of...
    Resource Type:
    Masters Thesis
    Full Text:
    . A medium-term experiment recorded weekly datasets from 5 subjects over the course of 6 weeks to

  • Uncertainty in Deep Learning with Implicit Neural Networks

    Creator:
    Ratzlaff, Neale
    Abstract:
    The ability to extract uncertainties from predictions is crucial for the adoption of deep learning systems to safety-critical applications. Uncertainty estimates can be used as a failure signal, which is necessary for automating complex tasks where safety is a concern. Furthermore, current deep learning systems do not provide uncertainty estimates,...
    Resource Type:
    Dissertation
    Full Text:
    variational inference that allows for efficient sampling, places no restrictions on the approximate posterior

  • The density signature

    Creator:
    Migler-VonDollen, Theresa
    Abstract:
    In real networks, identifying dense regions is of great importance. For example, in a network that represents academic collaboration, authors within the densest component of the graph tend to be the most prolific. Dense subgraphs often identify communities in social networks. And dense subgraphs can be used to discover regulatory...
    Resource Type:
    Dissertation
    Full Text:
    + (1 − t)y) < tf(x) + (1 − t)f(y) for any t ∈ (0, 1) and any x < y. Theorem 6 The algorithm Path

  • Improving automated email tagging with implicit feedback

    Creator:
    Sorower, Mohammad Shahed
    Abstract:
    Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...
    Resource Type:
    Dissertation
    Full Text:
    message, no corrections have been made, then perhaps it is safe for the learning system to treat the

  • Monte Carlo Counterfactual Regret Minimization applied to Clue

    Creator:
    Martin, Benjamin
    Abstract:
    This document analyzes the application of Monte Carlo Counterfactual Regret Minimization (MCCFR) in the game of Hasboro’s Clue. As a partially observable stochastic multiplayer game, Clue is well-suited for MCCFR methods. MCCFR has previously been shown to be effective in beating top human players around the world in No-Limit Texas...
    Resource Type:
    Honors College Thesis
    Full Text:
    Dice, [6] No-Limits Texas Hold’Em, [5] and Kuhn Poker. We have extended the work from No-Limits Texas

  • Protein secondary structure prediction using conditional random fields and profiles

    Creator:
    Shen, Rongkun
    Abstract:
    Protein secondary structure prediction plays a pivotal role in predicting protein folding in three-dimensions. Its task is to assign each residue one of the three secondary structure classes helix, strand, or random coil. This is an instance of the problem of sequential supervised learning in machine learning. This thesis describes...
    Resource Type:
    Masters Thesis
    Full Text:
    , respectively. In addition, there was no apparent overfitting problem observed in our model. Besides