research
∙
05/23/2023
Expressive Losses for Verified Robustness via Convex Combinations
In order to train networks for verified adversarial robustness, previous...
share
research
∙
05/17/2023
Provably Correct Physics-Informed Neural Networks
Recent work provides promising evidence that Physics-informed neural net...
research
∙
12/09/2022
DRIP: Domain Refinement Iteration with Polytopes for Backward Reachability Analysis of Neural Feedback Loops
Safety certification of data-driven control techniques remains a major o...
research
∙
06/29/2022
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound
Recent works have tried to increase the verifiability of adversarially t...
research
∙
04/14/2021
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition
We improve the scalability of Branch and Bound (BaB) algorithms for form...
research
∙
02/18/2021
Verifying Probabilistic Specifications with Functional Lagrangians
We propose a general framework for verifying input-output specifications...
research
∙
01/14/2021
Scaling the Convex Barrier with Active Sets
Tight and efficient neural network bounding is of critical importance fo...
research
∙
10/27/2020
An efficient nonconvex reformulation of stagewise convex optimization problems
Convex optimization problems with staged structure appear in several con...
research
∙
10/22/2020
Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming
Convex relaxations have emerged as a promising approach for verifying de...
research
∙
07/10/2020
Contrastive Training for Improved Out-of-Distribution Detection
Reliable detection of out-of-distribution (OOD) inputs is increasingly u...
research
∙
02/24/2020
Lagrangian Decomposition for Neural Network Verification
A fundamental component of neural network verification is the computatio...
research
∙
09/14/2019
Branch and Bound for Piecewise Linear Neural Network Verification
The success of Deep Learning and its potential use in many safety-critic...
research
∙
04/26/2019
Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications
Models such as Sequence-to-Sequence and Image-to-Sequence are widely use...
research
∙
02/25/2019
Verification of Non-Linear Specifications for Neural Networks
Prior work on neural network verification has focused on specifications ...
research
∙
10/30/2018
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
Recent works have shown that it is possible to train models that are ver...
research
∙
05/23/2018
Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials
Dense conditional random fields (CRFs) with Gaussian pairwise potentials...
research
∙
05/11/2018
Leveraging Grammar and Reinforcement Learning for Neural Program Synthesis
Program synthesis is the task of automatically generating a program cons...
research
∙
11/01/2017
Piecewise Linear Neural Network verification: A comparative study
The success of Deep Learning and its potential use in many important saf...
research
∙
10/11/2017
Neural Program Meta-Induction
Most recently proposed methods for Neural Program Induction work under t...
research
∙
11/29/2016
Efficient Linear Programming for Dense CRFs
The fully connected conditional random field (CRF) with Gaussian pairwis...
research
∙
08/22/2016
