*Correcting Deep Neural Networks with Small, Generalizing Patches*

Sotoudeh, Matthew and Thakur, Aditya V.NeurIPS 2019 Workshop on Safety and Robustness in Decision Making, 2019

We consider the problem of patching a deep neural network: applying a small change to the network weights in order to produce a desired change in the clas- sifications made by the network. We motivate this problem using ACAS Xu, a well-studied neural network intended to act as an aircraft collision-avoidance system. Our technique works over infinite patching regions, is based on an SMT formulation of the problem, and has a number of desirable convergence properties. To make this approach efficient, we introduce a symbolic representation of neural networks and a generalization of ReLU neural networks (Masking Networks). We show that our approach can produce highly effective and generalizing patches with a very small number of weight changes.

PDF@inproceedings{SRDM2019, author = {Sotoudeh, Matthew and Thakur, Aditya V.}, title = {Correcting Deep Neural Networks with Small, Generalizing Patches}, booktitle = {NeurIPS 2019 Workshop on Safety and Robustness in Decision Making}, year = {2019} }