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Filtering by: Creator DeDen, F. Michael Contributor Li, Fuxin License All rights reserved

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

Full Text:

. . . . . . . . . . . . . . . . . . 75 4.3 A diagram of our xGA model. G𝑟 , G𝑐, and F are fixed pretrained models. 𝛿𝑛 and 𝛿𝑛 are
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:

optimization problem of the form, minimize x,z f(x) + g(z) (2.3) subject to Ax+Bz = c ADMM, firstly, forms
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:

two-layer neural network with 8 1-norm weight constraints as: F = { x 7→ ∑ i viσ(wi · x) : ‖v