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Found 10 papers, 3 papers with code
Machine Learning Robustness: A Primer
This chapter explores the foundational concept of robustness in Machine Learning (ML) and its integral role in establishing trustworthiness in Artificial Intelligence (AI) systems.
An Intentional Forgetting-Driven Self-Healing Method For Deep Reinforcement Learning Systems
Dr. DRL successfully helps agents to adapt to 19. 63% of drifted environments left unsolved by vanilla CL while maintaining and even enhancing by up to 45% the obtained rewards for drifted environments that are resolved by both approaches.
SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design
Hybrid surrogate optimization maintains high results quality while providing rapid design assessments when both the surrogate model and the switch mechanism for eventually transitioning to the HF model are calibrated properly.
Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation
In the context of aircraft system performance assessment, deep learning technologies allow to quickly infer models from experimental measurements, with less detailed system knowledge than usually required by physics-based modeling.
DiverGet: A Search-Based Software Testing Approach for Deep Neural Network Quantization Assessment
Quantization is one of the most applied Deep Neural Network (DNN) compression strategies, when deploying a trained DNN model on an embedded system or a cell phone.
Testing Feedforward Neural Networks Training Programs
All these model training steps can be error-prone.
Models of Computational Profiles to Study the Likelihood of DNN Metamorphic Test Cases
We investigate the distribution of computational profile likelihood of metamorphic test cases with respect to the likelihood distributions of training, test and error control cases.
Faults in Deep Reinforcement Learning Programs: A Taxonomy and A Detection Approach
To allow for the automatic detection of faults in DRL programs, we have defined a meta-model of DRL programs and developed DRLinter, a model-based fault detection approach that leverages static analysis and graph transformations.
DeepEvolution: A Search-Based Testing Approach for Deep Neural Networks
To overcome these limitations, we propose, DeepEvolution, a novel search-based approach for testing DL models that relies on metaheuristics to ensure a maximum diversity in generated test cases.
TFCheck : A TensorFlow Library for Detecting Training Issues in Neural Network Programs
In this paper, we examine training issues in ML programs and propose a catalog of verification routines that can be used to detect the identified issues, automatically.
