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Found 19 papers, 3 papers with code
A Neurosymbolic Framework for Bias Correction in Convolutional Neural Networks
Given symbolic concepts, as ASP constraints, that the CNN is biased towards, we convert the concepts to their corresponding vector representations.
A Certified Proof Checker for Deep Neural Network Verification
Recent advances in the verification of deep neural networks (DNNs) have opened the way for broader usage of DNN verification technology in many application areas, including safety-critical ones.
NLP Verification: Towards a General Methodology for Certifying Robustness
We propose a number of practical NLP methods that can help to quantify the effects of the embedding gap; and in particular we propose the metric of falsifiability of semantic subspaces as another fundamental metric to be reported as part of the NLP verification pipeline.
Marabou 2.0: A Versatile Formal Analyzer of Neural Networks
This paper serves as a comprehensive system description of version 2. 0 of the Marabou framework for formal analysis of neural networks.
Vehicle: Bridging the Embedding Gap in the Verification of Neuro-Symbolic Programs
Neuro-symbolic programs -- programs containing both machine learning components and traditional symbolic code -- are becoming increasingly widespread.
Towards a Certified Proof Checker for Deep Neural Network Verification
In this work, we present a novel implementation of a proof checker for DNN verification.
ANTONIO: Towards a Systematic Method of Generating NLP Benchmarks for Verification
In particular, many known neural network verification methods that work for computer vision and other numeric datasets do not work for NLP.
Logic of Differentiable Logics: Towards a Uniform Semantics of DL
A DL consists of a syntax in which specifications are stated and an interpretation function that translates expressions in the syntax into loss functions.
CheckINN: Wide Range Neural Network Verification in Imandra (Extended)
Neural networks are increasingly relied upon as components of complex safety-critical systems such as autonomous vehicles.
Differentiable Logics for Neural Network Training and Verification
What difference does a specific choice of DL make in the context of continuous verification?
Why Robust Natural Language Understanding is a Challenge
With the proliferation of Deep Machine Learning into real-life applications, a particular property of this technology has been brought to attention: robustness Neural Networks notoriously present low robustness and can be highly sensitive to small input perturbations.
Network robustness as a mathematical property: training, evaluation and attack
We also perform experiments to compare the applicability and efficacy of different training methods for ensuring the network obeys these different definitions.
Actions You Can Handle: Dependent Types for AI Plans
Verification of AI is a challenge that has engineering, algorithmic and programming language components.
Neural Network Robustness as a Verification Property: A Principled Case Study
Neural networks are very successful at detecting patterns in noisy data, and have become the technology of choice in many fields.
Proof-Carrying Plans: a Resource Logic for AI Planning
In this paper, we present a novel resource logic, the Proof Carrying Plans (PCP) logic that can be used to verify plans produced by AI planners.
Neural Network Verification for the Masses (of AI graduates)
We will discuss future directions for incorporating verification into AI degrees.
ML4PG in Computer Algebra verification
ML4PG is a machine-learning extension that provides statistical proof hints during the process of Coq/SSReflect proof development.
Recycling Proof Patterns in Coq: Case Studies
Development of Interactive Theorem Provers has led to the creation of big libraries and varied infrastructures for formal proofs.
Machine Learning in Proof General: Interfacing Interfaces
We present ML4PG - a machine learning extension for Proof General.
