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Filtering by: Creator Phillips, Michael John Advisor Li, Fuxin Committee Member Fern, Alan

Uncertainty in Deep Learning with Implicit Neural Networks

Creator:

Ratzlaff, Neale

Abstract:

The ability to extract uncertainties from predictions is crucial for the adoption of deep learning systems to safety-critical applications. Uncertainty estimates can be used as a failure signal, which is necessary for automating complex tasks where safety is a concern. Furthermore, current deep learning systems do not provide uncertainty estimates,...

Resource Type:

Dissertation

Full Text:

, Christopher Buss, Lawrence Neal, Matthew Olson, Michael Slater, Robert DeBortoli, Risheek Garrepalli, Saeed
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
Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

Creator:

Khorram, Saeed

Abstract:

The advancement of artificial intelligence (AI) has led to transformative developments across multiple sectors, fostering innovation and redefining our interactions with technology. As AI matures and becomes integrated into society, it offers numerous opportunities to address global challenges and revolutionize a wide array of human endeavors. These advances are driven...

Resource Type:

Dissertation