16.2k citations · 16.4k across the 15 of their papers we have counts for
5 papers · 1 filter
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition
Alessandro De Palma, Rudy Bunel, Alban Desmaison +4
We improve the scalability of Branch and Bound (BaB) algorithms for formally proving input-output properties of neural networks. First, we propose novel bounding algorithms based o…
Lagrangian Decomposition for Neural Network Verification
Rudy Bunel, Alessandro De Palma, Alban Desmaison +4
A fundamental component of neural network verification is the computation of bounds on the values their outputs can take. Previous methods have either used off-the-shelf solvers, d…
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa +18
Deep learning frameworks have often focused on either usability or speed, but not both. PyTorch is a machine learning library that shows that these two goals are in fact compatible…
Learning to superoptimize programs - Workshop Version
Rudy Bunel, Alban Desmaison, M. Pawan Kumar +2
Superoptimization requires the estimation of the best program for a given computational task. In order to deal with large programs, superoptimization techniques perform a stochasti…
Learning to superoptimize programs
Rudy Bunel, Alban Desmaison, M. Pawan Kumar +2
Code super-optimization is the task of transforming any given program to a more efficient version while preserving its input-output behaviour. In some sense, it is similar to the p…