Wenjie Ruan

Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the $L_0$ Norm

2018 · arXiv (Cornell University)

Deployment of deep neural networks (DNNs) in safety- or security-critical systems requires provable guarantees on their correct behaviour. A common requirement is robustness to adversarial perturbations in a neighbourhood around an input. In this paper …

Related or Duplicate

2018

Plenty of research attempts target the automatic duplicate detection in Community Question Answering (CQA) systems and frame the task as a supervised learning problem on the question pairs. However, these methods rely on handcrafted features, …

Safety and Trustworthiness of Deep Neural Networks: A Survey

2018 · arXiv (Cornell University)

In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level intelligence on several long-standing tasks. With broader deployment of DNNs on various applications, the concerns on its …

3DVerifier: efficient robustness verification for 3D point cloud models

2022 · Machine Learning

Abstract 3D point cloud models are widely applied in safety-critical scenes, which delivers an urgent need to obtain more solid proofs to verify the robustness of models. Existing verification method for point cloud model is …