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Alexander Wiltschko

2 accepted papers

2020

Evaluating Attribution for Graph Neural Networks

NeurIPS 2020poster

Interpretability of machine learning models is critical to scientific understanding, AI safety, as well as debugging. Attribution is one approach to interpretability, which highlights input dimensions that are influential to a neural network’s prediction. Evaluation of these methods is largely quali…

2018

Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming

NeurIPS 2018poster

The need to efficiently calculate first- and higher-order derivatives of increasingly complex models expressed in Python has stressed or exceeded the capabilities of available tools. In this work, we explore techniques from the field of automatic differentiation (AD) that can give researchers expres…