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Poster
Interpreting Neural Network Judgments via Minimal, Stable, and Symbolic Corrections
Xin Zhang · Armando Solar-Lezama · Rishabh Singh

Tue Dec 04 07:45 AM -- 09:45 AM (PST) @ Room 210 #75
in Tue Poster Session A »

We present a new algorithm to generate minimal, stable, and symbolic corrections to an input that will cause a neural network with ReLU activations to change its output. We argue that such a correction is a useful way to provide feedback to a user when the network's output is different from a desired output. Our algorithm generates such a correction by solving a series of linear constraint satisfaction problems. The technique is evaluated on three neural network models: one predicting whether an applicant will pay a mortgage, one predicting whether a first-order theorem can be proved efficiently by a solver using certain heuristics, and the final one judging whether a drawing is an accurate rendition of a canonical drawing of a cat.

Keywords: Fairness, Accountability, and Transparency · Visualization or Exposition Techniques for Deep Networks
[ Paper]  [ Poster] 

Author Information

Xin Zhang (Massachusetts Institute of Technology)
Armando Solar-Lezama (MIT)
Rishabh Singh (Google Brain)

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