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20112026
most citedOn the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models

280 citations · 1.4k across the 75 of their papers we have counts for

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Showing 2020Show all

5 papers · 1 filter

cs.LG2020★ 15 cited

Autoencoding Variational Autoencoder

A. Taylan Cemgil, Sumedh Ghaisas, Krishnamurthy Dvijotham +2

Does a Variational AutoEncoder (VAE) consistently encode typical samples generated from its decoder? This paper shows that the perhaps surprising answer to this question is `No'; a…

cs.LG2020★ 2 cited

Towards transformation-resilient provenance detection of digital media

Jamie Hayes, Krishnamurthy, Dvijotham +4

Advancements in deep generative models have made it possible to synthesize images, videos and audio signals that are difficult to distinguish from natural signals, creating opportu…

cs.LG2020★ 14 cited

Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming

Sumanth Dathathri, Krishnamurthy Dvijotham, Alexey Kurakin +8

Convex relaxations have emerged as a promising approach for verifying desirable properties of neural networks like robustness to adversarial perturbations. Widely used Linear Progr…

math.OC2020★ 4 cited

An efficient nonconvex reformulation of stagewise convex optimization problems

Rudy Bunel, Oliver Hinder, Srinadh Bhojanapalli +2

Convex optimization problems with staged structure appear in several contexts, including optimal control, verification of deep neural networks, and isotonic regression. Off-the-she…

cs.LG2020★ 8 cited

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…