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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:

, 5 θ1 = arg max θ p(y|X, θ) = pmodel(y|X) (2.2) = arg max θ N∏ i=1 pmodel(yi|xi) = arg max θ N
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