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Posts: 3
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Probing Deep Network Behavior with Internal Influence

Klas Leino on deep learning, explainability, interpretability, xai, machine learning, neural networks | 18 Feb 2019

In recent work with my colleagues at CMU, we focus on bringing greater transparency to these mysterious, yet effective, machine learning techniques.…

Proxy-Based Approach to Fair Machine Learning

Samuel Yeom on machine learning, linear regression, fairness, proxy, explainability | 03 Dec 2018

We explain the motivation behind our 2018 NeurIPS paper, "Hunting for Discriminatory Proxies in Linear Regression Models".…

Explainability in Neural Networks, Part 2: Limitations of Simple Feature Attribution Methods

Prasad Chalasani on deep learning, explainability, neural networks, attribution, machine learning | 19 Oct 2018

We examine some simple, intuitive methods to explain the output of a neural network (based on perturbations and gradients), and see how they produce non-sensical results for non-linear functions.…

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