<2024-08-01 বৃহঃ>
In this article, we perform a detailed literature review of Formal Verification of DNN methods. We first aim to list out the papers relevant to it. We plan to address all years - from 2027 to 2023. We cover a total of 9 FM and AI related conferences for it.
| Paper name | Year | Conference |
|---|---|---|
| Certifying the Fairness of KNN in the Presence of Dataset Bias | 2023 | CAV |
| Monitoring Algorithmic Fairness | 2023 | CAV |
| nl2spec: Interactively Translating Unstructured Natural Language to Temporal Logics with Large Language Models | 2023 | CAV |
| NNV 2.0: The Neural Network Verification Tool | 2023 | CAV |
| QEBVerif: Quantization Error Bound Verification of Neural Networks | 2023 | CAV |
| Verifying Generalization in Deep Learning | 2023 | CAV |
| Formal Methods for Trusted AI | 2023 | FMCAD |
| Formally Explaining Neural Networks within Reactive Systems | 2023 | FMCAD |
| Lightweight Online Learning for Sets of Related Problems in Automated Reasoning | 2023 | FMCAD |
| DelBugV: Delta-Debugging Neural Network Verifiers | 2023 | FMCAD |
| Robustness Verification of DNN using Star-based reachability analysis with variable length time-series input | 2023 | FMICS |
| Verifying Feed-Forward neural network for classification in Isabelle/HOL | 2023 | FM |
| SMPT: A testbed for reachability methods in generalized petri nets | 2023 | FM |
| The octatope abstract domain for verification of Neural Network | 2023 | FM |
| Program semantics and verification techniques for AI-centered programs | 2023 | FM |
| Shifting left for early detection of ML bugs | 2023 | FM |
| Run time monitoring for out-of-distribution detection in object-detection neural network | 2023 | FM |
| Backdoor mitigation in neural network via strategic retraining | 2023 | FM |
| veriFIRE: Verifying an Industrial, Learning based wild fire detection system | 2023 | FM |
| Formalizing robustness against character level perturbations for Neural Network Language Models | 2023 | FM |
| Branch and bound method for sigmoid like neural network verification | 2023 | ICFEM |
| Certifying sequential consistency of machine learning accelerators | 2023 | ICFEM |
Verifying neural networks by approximating convex hull |
2023 | ICFEM |
| Eager to stop: Efficient falsification of Deep Neural Network | 2023 | ICFEM |
| Unified verification of neural network's robustness and privacy verification in computer vision | 2023 | ICFEM |
| IoT Software vulnerability detection through LLM (why is it FM? Check) | 2023 | ICFEM |
| Chronosymbolic Learning: Efficient CHC Solving with Symbolic Reasoning and Inductive Learning | 2023 | SAIV |
| Error Analysis of Shapley Value-Based Model Explanations: An Informative Perspective | 2023 | SAIV |
| Concept-Based Analysis of Neural Networks via Vision-Language Models | 2023 | SAIV |
| Parallel Verification for delta-Equivalence of Neural Network Quantization | 2023 | SAIV |
| Verification of Neural Network Control Systems in Continuous Time | 2023 | SAIV |
| A Preliminary Study to Examining Per-class Performance Bias via Robustness Distributions | 2023 | SAIV |
| Clover: Closed-Loop Verifiable Code Generation | 2023 | SAIV |
| Provable Repair of Vision Transformers | 2023 | SAIV |
| Iterative Counter-Example Guided Robustness Verification for Neural Networks | 2023 | SAIV |
| Alpha-beta-crown, FastBATLLNN, Marabou, NeuralSAT, nnenum, NNV, PyRAT | 2023 | VNN |
| Verifying Learning-Based Robotic Navigation Systems | 2023 | FoMLAS |
| DNN Verification, Reachability, and the Exponential Function Problem | 2023 | FoMLAS |
| Supporting Standardization of Neural Networks Verification with VNNLIB and CoCoNet | 2023 | FoMLAS |
| A Semidefinite Relaxation Based Branch-and-Bound Method for Tight Neural Network Verification | 2023 | FoMLAS |
| Prediction and Control of Stochastic Agents Using Formal Methods | 2023 | FoMLAS |
| Scaling Model Checking for Neural Network Analysis via State-Space Reduction and Input Segmentation | 2023 | FoMLAS |
| Towards a Study of Performance for Safe Neural Network Training | 2023 | FoMLAS |
| Verifying Global Neural Network Specifications using Hyperproperties | 2023 | FoMLAS |
| Robust Training of Neural Networks against Bias Field Perturbations | 2023 | FoMLAS |
| A Preliminary Study of Robustness Distributions for Neural Network Verification | 2023 | FoMLAS |
| Certified Private Inference on Neural Networks via Lipschitz-Guided Abstraction Refinement | 2023 | FoMLAS |
| Logic of Differentiable Logics: Towards a Uniform Semantics of DL | 2023 | FoMLAS |
| ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification | 2023 | FoMLAS |
| Model Checking the Optimal Behavior of Big Markov Process | 2023 | FoMLAS |
| Google Deepmind Article | ||
| Evaluating robustness of NN with Mixed Integer Programming | 2018 |