49 citations · 53 across the 3 of their papers we have counts for
6 papers
LinSyn: Synthesizing Tight Linear Bounds for Arbitrary Neural Network Activation Functions
Brandon Paulsen, Chao Wang
The most scalable approaches to certifying neural network robustness depend on computing sound linear lower and upper bounds for the network's activation functions. Current approac…
NeuroDiff: Scalable Differential Verification of Neural Networks using Fine-Grained Approximation
Brandon Paulsen, Jingbo Wang, Jiawei Wang +1
As neural networks make their way into safety-critical systems, where misbehavior can lead to catastrophes, there is a growing interest in certifying the equivalence of two structu…
DiffRNN: Differential Verification of Recurrent Neural Networks
Sara Mohammadinejad, Brandon Paulsen, Chao Wang +1
Recurrent neural networks (RNNs) such as Long Short Term Memory (LSTM) networks have become popular in a variety of applications such as image processing, data classification, spee…
ReluDiff: Differential Verification of Deep Neural Networks
Brandon Paulsen, Jingbo Wang, Chao Wang
As deep neural networks are increasingly being deployed in practice, their efficiency has become an important issue. While there are compression techniques for reducing the network…
Debreach: Mitigating Compression Side Channels via Static Analysis and Transformation
Brandon Paulsen, Chungha Sung, Peter A. H. Peterson +1
Compression is an emerging source of exploitable side-channel leakage that threatens data security, particularly in web applications where compression is indispensable for performa…
CANAL: A Cache Timing Analysis Framework via LLVM Transformation
Chungha Sung, Brandon Paulsen, Chao Wang
A unified modeling framework for non-functional properties of a program is essential for research in software analysis and verification, since it reduces burdens on individual rese…