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Found 5 papers, 0 papers with code
Adversarial Robustness of Deep Neural Networks: A Survey from a Formal Verification Perspective
For those reasons, there is a high demand for trustworthy and rigorous methods to verify the robustness of neural network models.
MUC-driven Feature Importance Measurement and Adversarial Analysis for Random Forest
The broad adoption of Machine Learning (ML) in security-critical fields demands the explainability of the approach.
An Executable Formal Model of the VHDL in Isabelle/HOL
To enable formal reasoning in one of the most commonly used description language VHDL, we define a formal model of the VHDL language in Isabelle/HOL.
GRAVITAS: A Model Checking Based Planning and Goal Reasoning Framework for Autonomous Systems
We follow the "verification as planning" paradigm and propose to use model checking techniques to solve planning and goal reasoning problems for autonomous systems.
Silas: High Performance, Explainable and Verifiable Machine Learning
This paper introduces a new classification tool named Silas, which is built to provide a more transparent and dependable data analytics service.
