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Rudy Bunel

7 accepted papers

2020

Lagrangian Decomposition for Neural Network Verification

UAI 2020poster

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, discarding the problem structure, or relaxed the problem even further, making the bounds unnecessarily loose. We propose…

2019

Knowing When to Stop: Evaluation and Verification of Conformity to Output-Size Specifications

CVPR 2019poster

Neural architectures able to generate variable-length outputs are extremely effective for applications like Machine Translation and Image Captioning. In this paper, we study the vulnerability of these models to attacks aimed at changing the output-size that can have undesirable consequences includin…

Cited by 13PDFScholar
2019

Scalable Verified Training for Provably Robust Image Classification

ICCV 2019poster

Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations. Most of these methods are based on minimizing an upper bound on the worst-case loss over all possible adversarial perturbations. While these techniques show pro…

Cited by 214PDFScholar
2019

Verification of Non-Linear Specifications for Neural Networks

ICLR 2019poster

Prior work on neural network verification has focused on specifications that are linear functions of the output of the network, e.g., invariance of the classifier output under adversarial perturbations of the input. In this paper, we extend verification algorithms to be able to certify richer proper…

Cited by 50SourcePDFScholar
2018

Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis

ICLR 2018poster

Program synthesis is the task of automatically generating a program consistent with a specification. Recent years have seen proposal of a number of neural approaches for program synthesis, many of which adopt a sequence generation paradigm similar to neural machine translation, in which sequence-to-…

Cited by 257SourcePDFScholar
2017

Efficient Linear Programming for Dense CRFs

CVPR 2017poster

The fully connected conditional random field (CRF) with Gaussian pairwise potentials has proven popular and effective for multi-class semantic segmentation. While the energy of a dense CRF can be minimized accurately using a linear programming (LP) relaxation, the state-of-the-art algorithm is too s…

Cited by 21PDFScholar