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Filtering by: Alternative title ssim WRRI-10 Remove constraint Alternative title ssim: WRRI-10 Committee Member Fern, Alan Remove constraint Committee Member: Fern, Alan Resource Type Dissertation Remove constraint Resource Type: Dissertation

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  • Network coding for multi-user wireless networks

    Creator:
    Nguyen, Dong Van
    Abstract:
    Until a few years ago, wireless-capable laptops were considered novelties by many. It is now hard to find a laptop or a hand-held computing device that is not wireless-ready. As wireless devices are becoming commodities, they have also become an indispensable part of the modern society. Not surprisingly, research in...
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    Dissertation
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    of Doctor of Philosophy in Electrical Engineering and Computer Science presented on June 10, 2009

  • Navigation and coordination of autonomous mobile robots with limited resources

    Creator:
    Knudson, Matthew D.
    Abstract:
    The use of autonomous robots in complex exploration tasks is rapidly increasing. Indeed, robots can provide speed and cost effectiveness in many tasks, as well as allow operation in environments that are hostile to humans. In this dissertation we: 1) provide two adaptive navigation algorithms; 2) develop a coordination mechanism;...
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    Coordination . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Dynamic Partnership Formation

  • From multitarget tracking to event recognition in videos

    Creator:
    Brendel, William
    Abstract:
    This dissertation addresses two fundamental problems in computer vision—namely, multitarget tracking and event recognition in videos. These problems are challenging because uncertainty may arise from a host of sources, including motion blur, occlusions, and dynamic cluttered backgrounds. We show that these challenges can be successfully addressed by using a multiscale,...
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    solutions. However, detecting human activities in real-world videos is very difficult [60, 10, 36, 73, 53

  • From shape-based object recognition and discovery to 3D scene interpretation

    Creator:
    Payet, Nadia
    Abstract:
    This dissertation addresses a number of inter-related and fundamental problems in computer vision. Specifically, we address object discovery, recognition, segmentation, and 3D pose estimation in images, as well as 3D scene reconstruction and scene interpretation. The key ideas behind our approaches include using shape as a basic object feature, and...
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    object properties [10]. Nevertheless, most recognition sys- tems resort to appearance features [21,77

  • 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...
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    . . . . . . . . . . . . . . . . . . . . . . . . . . 58 3.3 Front page of the interface for the cluster-to-cluster exploration. Images in Cluster #10

  • Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

    Creator:
    Khorram, Saeed
    Abstract:
    The advancement of artificial intelligence (AI) has led to transformative developments across multiple sectors, fostering innovation and redefining our interactions with technology. As AI matures and becomes integrated into society, it offers numerous opportunities to address global challenges and revolutionize a wide array of human endeavors. These advances are driven...
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    to accelerate convergence up to 10 times compared to baselines. We also introduce iGOS++, which

  • 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...
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    Dissertation
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    sequence (MSA size ≤ 10), we find that the iterative process of realigning the input sequences and output

  • 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,...
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    . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.1.3 Particle-based Variational Inference . . . . . . . . . . . . . . . . . 11 2.1.4 Comparison

  • Probabilistic models for quality control in environmental sensor networks

    Creator:
    Dereszynski, Ethan W.
    Abstract:
    Networks of distributed, remote sensors are providing ecological scientists with a view of our environment that is unprecedented in detail. However, these networks are subject to harsh conditions, which lead to malfunctions in individual sensors and failures in network communications. This behavior manifests as corrupt or missing measurements in the...
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    Dissertation
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    Manuscript One 8 2.1 Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

  • Weakly Supervised Learning for Activity Recognition from Time Series Data

    Creator:
    Guan, Xinze
    Abstract:
    The thesis focuses on activity recognition from sensor data, which has spurred a great deal of interest due to its impact on health care and security. Previous work on activity recognition from multivariate time series data has mainly applied supervised learning techniques which require a high degree of annotation effort...
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    SWEMMemberTrain(T, L) . . . . . . . . . . . . . . . . . . . . . . . . . 10 2 SWEMPredict(M, t