Search Results for author:
Found 11 papers, 3 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.
Supervised Robustness-preserving Data-free Neural Network Pruning
This may be unrealistic in practice, as the data controllers are often reluctant to provide their model consumers with the original data.
A Prompting-based Approach for Adversarial Example Generation and Robustness Enhancement
Our attack technique targets the inherent vulnerabilities of NLP models, allowing us to generate samples even without interacting with the victim NLP model, as long as it is based on pre-trained language models (PLMs).
DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training
Moreover, our case study shows that our visual solution can well reflect the characteristics of various training scenarios, showing good potential of DVI as a debugging tool for analyzing deep learning training processes.
Repairing Adversarial Texts through Perturbation
Furthermore, such attacks are impossible to eliminate, i. e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training.
Generalizing Neural Networks by Reflecting Deviating Data in Production
However, inputs may deviate from the training dataset distribution in real deployments.
Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling
In this work, we bridge the gap by proposing a scalable and effective approach for systematically searching for discriminatory samples while extending existing fairness testing approaches to address a more challenging domain, i. e., text classification.
There is Limited Correlation between Coverage and Robustness for Deep Neural Networks
In this work, we conduct an empirical study to evaluate the relationship between coverage, robustness and attack/defense metrics for DNN.
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.
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.
Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction
In this work, we propose an approach to extract probabilistic automata for interpreting an important class of neural networks, i. e., recurrent neural networks.
