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Filtering by: Creator Brooks, J. R. Remove constraint Creator: Brooks, J. R. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad Decade 2020-2029 Remove constraint Decade: 2020-2029

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  • An Information Modeling Framework for Support of Sustainable Manufacturing System Design Decision Making

    Creator:
    Shankar Raman, Arvind
    Abstract:
    Manufacturing technology has continuously evolved and advanced over the past century; this has led to an increase in the production of consumer and industrial goods driven by simultaneous growth in population and wealth. Despite the resulting economic and labor growth, environmental impacts of manufacturing have increased dramatically due to the...
    Resource Type:
    Dissertation
    Full Text:
    : _____________________________________________________ Karl R. Haapala Manufacturing technology has continuously evolved and advanced over

  • Advancing Power Grid Computing: Analysis and Synthesis of Wide-Area Data, Distributed Real-Time Hardware-In-the-Loop Simulations, and Machine Learning for Event Detection

    Creator:
    Hagan, Travis
    Abstract:
    Global Positioning Systems have allowed for precise timing of power system measurements over wide areas. This newly found capability has the potential to provide much greater insight into the operation of the power system and its response to contingencies, but few analytical techniques currently exist that provide enough robustness and...
    Resource Type:
    Dissertation
    Full Text:
    (�%������������������������ A6 ��)IF&�+'#2�!�'#,�%��H2'��'"�!&(�%����������������������� A6 ��)�J'!!:�"���#(%���#�#��++���+42

  • Analytical Methods for Improving Causal Inference

    Creator:
    Sinha, Meghamala
    Abstract:
    Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
    Resource Type:
    Dissertation
    Full Text:
    distribution of p after intervention is: p(V1, ..., Vn) = ∏ Vi∈Oe pi(Vi | Pa(Vi)) · ∏ Vj∈Ie p′j(Vj | Pa(Vj

  • Visual object discovery and understanding

    Creator:
    Yuan, Jialin
    Abstract:
    Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but...
    Resource Type:
    Dissertation
    Full Text:
    almost as good as mask R-CNN without depending on a two-stage framework. Note that the training of the

  • Coordination for Scalable Multiple Robot Planning Under Temporal Uncertainty

    Creator:
    Marcon dos Santos, Gilberto
    Abstract:
    This dissertation incorporates coalition formation and probabilistic planning towards a domain-independent automated planning solution scalable to multiple heterogeneous robots in complex domains. The first research direction investigates the effectiveness of Task Fusion and introduces heuristics that improve task allocation and result in better quality plans, while requiring lower computational cost...
    Resource Type:
    Dissertation

  • Leveraging Structures of the Data in Deep Learning

    Creator:
    Li, Xingyi
    Abstract:
    The performance of deep learning frameworks could be significantly improved through considering the particular underlying structures for each dataset. In this thesis, I summarize our three work about boosting the performance of deep learning models through leveraging structures of the data. In the first work, we theoretically justify that, for...
    Resource Type:
    Dissertation
    Full Text:
    non-negative convex increasing function F on R+, EF (max i,j |Yi − Yj |) ≤ EF (max i,j |Xi −Xj

  • Knowledge Base Completion Leveraging Natural Language Data

    Creator:
    Shahbazi, Hamed
    Abstract:
    Information about named entities (real-world objects) is usually harvested from different sources and organized as a multiple relational directed graph in Knowledge Bases (KBs). KBs play essential roles in many NLP modules including question answering, fact-checking, search engines, etc. KBs are big but still incomplete: relational information among entities is...
    Resource Type:
    Dissertation
    Full Text:
    , ...yn] of referent entities to maximize the following score: s(Y) = n∑ i=1 ψ(xi, yi) + n∑ i=1 n∑ j=1

  • Scheduling and Online Planning in Stochastic Diffusion Networks

    Creator:
    Xue, Shan
    Abstract:
    Diffusion processes in networks are common models for many domains, including species colonization, information/idea cascade, disease propagation and fire spreading. In diffusion networks, a diffusion event occurs when a behavior spreads from one node to the other following a probabilistic model, where the behavior could be species, an idea, a...
    Resource Type:
    Dissertation
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    vertex r, a set of terminal vertices T , and a set of edges sets E = {Es : s = 1, . . . ,M}, where each

  • 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:
    ) label scores of the resulting span: ξ = maxX s(j, j+1, X) and σ = maxX s(k, j,X) where X is a

  • Learning and Improving Policies for Probabilistic Planning Problems

    Creator:
    Issakkimuthu, Murugeswari
    Abstract:
    In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...
    Resource Type:
    Dissertation
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    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.4 FC-DRP vs S-DRP vs R-DRP Results . . . . . . . . . . . . . . . . . . . . 19 3.1 Game-of-Life