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Filtering by: Creator Bottomley, Peter J. Committee Member Fern, Alan Contributor Li, Fuxin

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:

M ,S 1 2 ‖X −MS‖2F (2.8) subject to Mij ≥ 0, Sij ≥ 0 ∀i, j where r is the decomposition rank and
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