Pith. sign in

REVIEW 4 cited by

The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Results

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2109.00498 v1 pith:UNHISP5J submitted 2021-08-31 cs.LO cs.LG

Stanley Bak , Changliu Liu , Taylor Johnson This is my paper · ORCID
classification cs.LOcs.LG
keywords competitionneuralverificationinternationalnetworksmethodsreportresults
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This report summarizes the second International Verification of Neural Networks Competition (VNN-COMP 2021), held as a part of the 4th Workshop on Formal Methods for ML-Enabled Autonomous Systems that was collocated with the 33rd International Conference on Computer-Aided Verification (CAV). Twelve teams participated in this competition. The goal of the competition is to provide an objective comparison of the state-of-the-art methods in neural network verification, in terms of scalability and speed. Along this line, we used standard formats (ONNX for neural networks and VNNLIB for specifications), standard hardware (all tools are run by the organizers on AWS), and tool parameters provided by the tool authors. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this competition.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 43 citations worldwide. Full citation record

  1. Training Verification-Friendly Neural Networks via Neuron Behavior Consistency

    cs.LG 2024-12 conditional novelty 6.0 of 10

    A neuron behavior consistency loss reduces the number of unstable neurons and speeds up formal verification of trained networks across MNIST, Fashion-MNIST, and CIFAR-10.

  2. BaB-ND: Long-Horizon Motion Planning with Branch-and-Bound and Neural Dynamics

    cs.RO 2024-12 conditional novelty 6.0 of 10

    A GPU-accelerated branch-and-bound planner over neural dynamics models uses adapted CROWN bounds to prune subdomains and beat sampling-based and MIP baselines on long-horizon manipulation tasks.

  3. Neural Network Verification is a Programming Language Challenge

    cs.PL 2025-01 conditional novelty 4.0 of 10

    Neural network verification's hardest open problems are reframed as programming language design challenges, with a unified dependently typed language proposed as the ideal solution.

  4. Creating a Formally Verified Neural Network for Autonomous Navigation: An Experience Report

    cs.LO 2024-11 conditional novelty 4.0 of 10

    A case study shows that differentiable-logic training improves local robustness of a small path-centring network, but current verifiers fail on the regression architecture and the title's 'formally verified' claim is ...

Pith tools