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Filtering by: Creator Hansen, E. M. Remove constraint Creator: Hansen, E. M. Academic Affiliation Electrical Engineering and Computer Science Remove constraint Academic Affiliation: Electrical Engineering and Computer Science Contributor Fu, Xiao Remove constraint Contributor: Fu, Xiao

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  • Graph Imitation Learning for Optimal Joint Beamforming and Antenna Selection

    Creator:
    Shrestha, Sagar
    Abstract:
    Transmit beamforming is an important technique employed to improve efficiency and signal quality in wireless communication systems by steering signals towards their in- tended users. It often arises jointly with the antenna selection problem due to various reasons, such as limited number of radio frequency (RF) chains and energy/resource effi-...
    Resource Type:
    Masters Thesis
    Full Text:
    ,M,L) = (8, 4, 4). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 LIST OF TABLES

  • Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

    Creator:
    Khorram, Saeed
    Abstract:
    In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...
    Resource Type:
    Masters Thesis
    Full Text:
    ) Pixel-level probability Sn,mip,jp ; (b) Voronoi-based probability S n,m p for the example image in

  • Machine Learning Techniques to Model Species Interactions and Distributions Under Imperfect Detection

    Creator:
    Seo, Eugene
    Abstract:
    Ecological domains seeking to understand the environment and the behavior of species have received little attention in machine learning (ML), despite the fact that environmental changes have a significant impact on humans as well as ecosystems. Some ecological problems can be formulated similarly to other common ML applications, but there...
    Resource Type:
    Dissertation
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 D Proof of Proposition 1 . . . . . . . . . . . . . . . . . . . . . . . . . . 141 E Parameter