Matthew Kowal
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April 4, 2022

Motivating Interpretability - A Researcher and Engineering Perspective

Deep neural networks (DNNs) are known to be almost fully opaque compared to traditional algorithms, even to top engineers and scientists who use DNNs. Many reasons have been proposed for...

interpretability deep learning
January 19, 2020

Understanding Mutual Information

For this post I hope to accomplish a few different things. I will review a brilliant document put together by Erik G. Learned-Miller. I found this document when attempting to...

mutual information information theory representation learning self-supervised learning
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