17 citations · 45 across the 7 of their papers we have counts for
6 papers · 1 filter
Architecture-Preserving Provable Repair of Deep Neural Networks
Zhe Tao, Stephanie Nawas, Jacqueline Mitchell +1
Deep neural networks (DNNs) are becoming increasingly important components of software, and are considered the state-of-the-art solution for a number of problems, such as image rec…
Provable Repair of Deep Neural Networks
Matthew Sotoudeh, Aditya V. Thakur
Deep Neural Networks (DNNs) have grown in popularity over the past decade and are now being used in safety-critical domains such as aircraft collision avoidance. This has motivated…
SyReNN: A Tool for Analyzing Deep Neural Networks
Matthew Sotoudeh, Aditya V. Thakur
Deep Neural Networks (DNNs) are rapidly gaining popularity in a variety of important domains. Formally, DNNs are complicated vector-valued functions which come in a variety of size…
Abstract Neural Networks
Matthew Sotoudeh, Aditya V. Thakur
Deep Neural Networks (DNNs) are rapidly being applied to safety-critical domains such as drone and airplane control, motivating techniques for verifying the safety of their behavio…
A Symbolic Neural Network Representation and its Application to Understanding, Verifying, and Patching Networks
Matthew Sotoudeh, Aditya V. Thakur
Analysis and manipulation of trained neural networks is a challenging and important problem. We propose a symbolic representation for piecewise-linear neural networks and discuss i…
Computing Linear Restrictions of Neural Networks
Matthew Sotoudeh, Aditya V. Thakur
A linear restriction of a function is the same function with its domain restricted to points on a given line. This paper addresses the problem of computing a succinct representatio…