activity
20152025
most citedA Dual Approach to Scalable Verification of Deep Networks

167 citations · 389 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2025★ 87 cited

Accelerating scientific discovery with Co-Scientist

Juraj Gottweis, Wei-Hung Weng, Alexander Daryin +48

Scientific discovery is driven by scientists generating novel hypotheses for complex problems that undergo rigorous experimental validation. To augment this process, we introduce C…

cs.LG2019★ 9 cited

Verification of Non-Linear Specifications for Neural Networks

Chongli Qin, Krishnamurthy, Dvijotham +7

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 adve…

cs.LG2018★ 43 cited

Rigorous Agent Evaluation: An Adversarial Approach to Uncover Catastrophic Failures

Jonathan Uesato, Ananya Kumar, Csaba Szepesvari +7

This paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. We focus o…

cs.LG2018★ 167 cited

A Dual Approach to Scalable Verification of Deep Networks

Krishnamurthy, Dvijotham, Robert Stanforth +3

This paper addresses the problem of formally verifying desirable properties of neural networks, i.e., obtaining provable guarantees that neural networks satisfy specifications rela…

cs.CV2015★ 83 cited

SemanticPaint: A Framework for the Interactive Segmentation of 3D Scenes

Stuart Golodetz, Michael Sapienza, Julien P. C. Valentin +9

We present an open-source, real-time implementation of SemanticPaint, a system for geometric reconstruction, object-class segmentation and learning of 3D scenes. Using our system,…