b'Wenjie Ruan'

research

∙ 04/03/2023

Model-Agnostic Reachability Analysis on Deep Neural Networks

Verification plays an essential role in the formal analysis of safety-cr...

0 Chi Zhang, et al. ∙

research

∙ 03/03/2023

RePreM: Representation Pre-training with Masked Model for Reinforcement Learning

Inspired by the recent success of sequence modeling in RL and the use of...

0 Yuanying Cai, et al. ∙

research

∙ 01/29/2023

Towards Verifying the Geometric Robustness of Large-scale Neural Networks

Deep neural networks (DNNs) are known to be vulnerable to adversarial ge...

0 Fu Wang, et al. ∙

research

∙ 01/28/2023

Reachability Analysis of Neural Network Control Systems

Neural network controllers (NNCs) have shown great promise in autonomous...

0 Chi Zhang, et al. ∙

research

∙ 01/17/2023

Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients: a deep-learning-based study on a real-world longitudinal follow-up dataset

Objective: Peritoneal Dialysis (PD) is one of the most widely used life-...

0 Liantao Ma, et al. ∙

research

∙ 12/22/2022

Certified Policy Smoothing for Cooperative Multi-Agent Reinforcement Learning

Cooperative multi-agent reinforcement learning (c-MARL) is widely applie...

0 Ronghui Mu, et al. ∙

research

∙ 09/05/2022

Adversarial Detection: Attacking Object Detection in Real Time

Intelligent robots rely on object detection models to perceive the envir...

0 Han Wu, et al. ∙

research

∙ 08/01/2022

Understanding Adversarial Robustness of Vision Transformers via Cauchy Problem

Recent research on the robustness of deep learning has shown that Vision...

0 Zheng Wang, et al. ∙

research

∙ 07/17/2022

DIMBA: Discretely Masked Black-Box Attack in Single Object Tracking

The adversarial attack can force a CNN-based model to produce an incorre...

0 Xiangyu Yin, et al. ∙

research

∙ 07/15/2022

3DVerifier: Efficient Robustness Verification for 3D Point Cloud Models

3D point cloud models are widely applied in safety-critical scenes, whic...

0 Ronghui Mu, et al. ∙

research

∙ 07/05/2022

PRoA: A Probabilistic Robustness Assessment against Functional Perturbations

In safety-critical deep learning applications robustness measurement is ...

0 Tianle Zhang, et al. ∙

research

∙ 11/10/2021

Sparse Adversarial Video Attacks with Spatial Transformations

In recent years, a significant amount of research efforts concentrated o...

0 Ronghui Mu, et al. ∙

research

∙ 08/24/2021

Adversarial Robustness of Deep Learning: Theory, Algorithms, and Applications

This tutorial aims to introduce the fundamentals of adversarial robustne...

0 Wenjie Ruan, et al. ∙

research

∙ 08/03/2021

Tutorials on Testing Neural Networks

Deep learning achieves remarkable performance on pattern recognition, bu...

0 Nicolas Berthier, et al. ∙

research

∙ 03/16/2021

Adversarial Driving: Attacking End-to-End Autonomous Driving Systems

As the research in deep neural networks advances, deep convolutional net...

0 Han Wu, et al. ∙

research

∙ 03/04/2021

Gradient-Guided Dynamic Efficient Adversarial Training

Adversarial training is arguably an effective but time-consuming way to ...

0 Fu Wang, et al. ∙

research

∙ 01/04/2021

Fooling Object Detectors: Adversarial Attacks by Half-Neighbor Masks

Although there are a great number of adversarial attacks on deep learnin...

0 Yanghao Zhang, et al. ∙

research

∙ 10/15/2020

Generalizing Universal Adversarial Attacks Beyond Additive Perturbations

The previous study has shown that universal adversarial attacks can fool...

6 Yanghao Zhang, et al. ∙

research

∙ 09/30/2020

Is AI Model Interpretable to Combat with COVID? An Empirical Study on Severity Prediction Task

Black-box nature hinders the deployment of many high-accuracy models in ...

0 Han Wu, et al. ∙

research

∙ 09/13/2020

Towards the Quantification of Safety Risks in Deep Neural Networks

Safety concerns on the deep neural networks (DNNs) have been raised when...

0 Peipei Xu, et al. ∙

research

∙ 07/17/2020

CovidCare: Transferring Knowledge from Existing EMR to Emerging Epidemic for Interpretable Prognosis

Due to the characteristics of COVID-19, the epidemic develops rapidly an...

12 Liantao Ma, et al. ∙

research

∙ 04/25/2020

Enabling Cost-Effective Population Health Monitoring By Exploiting Spatiotemporal Correlation: An Empirical Study

Because of its important role in health policy-shaping, population healt...

0 Dawei Chen, et al. ∙

research

∙ 11/27/2019

ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context

Predicting the patient's clinical outcome from the historical electronic...

35 Liantao Ma, et al. ∙

research

∙ 11/27/2019

AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration

Deep learning-based health status representation learning and clinical p...

0 Liantao Ma, et al. ∙

research

∙ 12/18/2018

Safety and Trustworthiness of Deep Neural Networks: A Survey

In the past few years, significant progress has been made on deep neural...

0 Xiaowei Huang, et al. ∙

research

∙ 07/10/2018

A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees

Despite the improved accuracy of deep neural networks, the discovery of ...

6 Min Wu, et al. ∙

research

∙ 05/06/2018

Reachability Analysis of Deep Neural Networks with Provable Guarantees

Verifying correctness of deep neural networks (DNNs) is challenging. We ...

0 Wenjie Ruan, et al. ∙

research

∙ 04/30/2018

Concolic Testing for Deep Neural Networks

Concolic testing alternates between CONCrete program execution and symbO...

0 Youcheng Sun, et al. ∙

research

∙ 04/16/2018

Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for L0 Norm

Deployment of deep neural networks (DNNs) in safety or security-critical...

0 Wenjie Ruan, et al. ∙

Wenjie Ruan

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