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09/06/2023
On Reducing Undesirable Behavior in Deep Reinforcement Learning Models
Deep reinforcement learning (DRL) has proven extremely useful in a large...
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07/31/2023
Formally Explaining Neural Networks within Reactive Systems
Deep neural networks (DNNs) are increasingly being used as controllers i...
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07/12/2023
Towards a Certified Proof Checker for Deep Neural Network Verification
Recent developments in deep neural networks (DNNs) have led to their ado...
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05/29/2023
DelBugV: Delta-Debugging Neural Network Verifiers
Deep neural networks (DNNs) are becoming a key component in diverse syst...
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05/10/2023
DNN Verification, Reachability, and the Exponential Function Problem
Deep neural networks (DNNs) are increasingly being deployed to perform s...
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02/11/2023
Verifying Generalization in Deep Learning
Deep neural networks (DNNs) are the workhorses of deep learning, which c...
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01/27/2023
OccRob: Efficient SMT-Based Occlusion Robustness Verification of Deep Neural Networks
Occlusion is a prevalent and easily realizable semantic perturbation to ...
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01/19/2023
Enhancing Deep Learning with Scenario-Based Override Rules: a Case Study
Deep neural networks (DNNs) have become a crucial instrument in the soft...
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01/05/2023
gRoMA: a Tool for Measuring Deep Neural Networks Global Robustness
Deep neural networks (DNNs) are a state-of-the-art technology, capable o...
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12/06/2022
veriFIRE: Verifying an Industrial, Learning-Based Wildfire Detection System
In this short paper, we present our ongoing work on the veriFIRE project...
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11/21/2022
BBReach: Tight and Scalable Black-Box Reachability Analysis of Deep Reinforcement Learning Systems
Reachability analysis is a promising technique to automatically prove or...
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11/16/2022
Efficiently Finding Adversarial Examples with DNN Preprocessing
Deep Neural Networks (DNNs) are everywhere, frequently performing a fair...
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10/25/2022
Towards Formal XAI: Formally Approximate Minimal Explanations of Neural Networks
With the rapid growth of machine learning, deep neural networks (DNNs) a...
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10/23/2022
Tighter Abstract Queries in Neural Network Verification
Neural networks have become critical components of reactive systems in v...
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08/16/2022
On Optimizing Back-Substitution Methods for Neural Network Verification
With the increasing application of deep learning in mission-critical sys...
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08/05/2022
Neural Network Verification using Residual Reasoning
With the increasing integration of neural networks as components in miss...
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06/20/2022
Constrained Reinforcement Learning for Robotics via Scenario-Based Programming
Deep reinforcement learning (DRL) has achieved groundbreaking successes ...
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06/01/2022
Neural Network Verification with Proof Production
Deep neural networks (DNNs) are increasingly being employed in safety-cr...
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05/26/2022
Verifying Learning-Based Robotic Navigation Systems
Deep reinforcement learning (DRL) has become a dominant deep-learning pa...
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03/19/2022
Efficient Neural Network Analysis with Sum-of-Infeasibilities
Inspired by sum-of-infeasibilities methods in convex optimization, we pr...
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02/09/2022
Scenario-Assisted Deep Reinforcement Learning
Deep reinforcement learning has proven remarkably useful in training age...
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02/08/2022
Verification-Aided Deep Ensemble Selection
Deep neural networks (DNNs) have become the technology of choice for rea...
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01/06/2022
An Abstraction-Refinement Approach to Verifying Convolutional Neural Networks
Convolutional neural networks have gained vast popularity due to their e...
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10/21/2021
RoMA: a Method for Neural Network Robustness Measurement and Assessment
Neural network models have become the leading solution for a large varie...
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10/18/2021
Minimal Multi-Layer Modifications of Deep Neural Networks
Deep neural networks (DNNs) have become increasingly popular in recent y...
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05/28/2021
Pruning and Slicing Neural Networks using Formal Verification
Deep neural networks (DNNs) play an increasingly important role in vario...
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05/25/2021
Towards Scalable Verification of RL-Driven Systems
Deep neural networks (DNNs) have gained significant popularity in recent...
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01/10/2021
Towards Repairing Scenario-Based Models with Rich Events
Repairing legacy systems is a difficult and error-prone task: often, lim...
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11/05/2020
An SMT-Based Approach for Verifying Binarized Neural Networks
Deep learning has emerged as an effective approach for creating modern s...
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10/07/2020
Global Optimization of Objective Functions Represented by ReLU Networks
Neural networks (NN) learn complex non-convex functions, making them des...
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06/06/2020
Guarded Deep Learning using Scenario-Based Modeling
Deep neural networks (DNNs) are becoming prevalent, often outperforming ...
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04/17/2020
Parallelization Techniques for Verifying Neural Networks
Inspired by recent successes with parallel optimization techniques for s...
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04/06/2020
Verifying Recurrent Neural Networks using Invariant Inference
Deep neural networks are revolutionizing the way complex systems are dev...
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10/31/2019
An Abstraction-Based Framework for Neural Network Verification
Deep neural networks are increasingly being used as controllers for safe...
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10/25/2019
Simplifying Neural Networks with the Marabou Verification Engine
Deep neural network (DNN) verification is an emerging field, with divers...
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09/01/2019
On-the-Fly Construction of Composite Events in Scenario-Based Modeling using Constraint Solvers
Scenario-Based Programming is a methodology for modeling and constructin...
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01/18/2018
Toward Scalable Verification for Safety-Critical Deep Networks
The increasing use of deep neural networks for safety-critical applicati...
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10/02/2017
DeepSafe: A Data-driven Approach for Checking Adversarial Robustness in Neural Networks
Deep neural networks have become widely used, obtaining remarkable resul...
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09/29/2017
Ground-Truth Adversarial Examples
The ability to deploy neural networks in real-world, safety-critical sys...
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09/08/2017
Towards Proving the Adversarial Robustness of Deep Neural Networks
Autonomous vehicles are highly complex systems, required to function rel...
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02/03/2017
