58 citations · 190 across the 14 of their papers we have counts for
15 papers
Dynamic Classification: Leveraging Self-Supervised Classification to Enhance Prediction Performance
Ziyuan Zhong, Junyang Zhou
In this study, we propose an innovative dynamic classification algorithm aimed at achieving zero missed detections and minimal false positives,acritical in safety-critical domains…
CAMEO: A Causal Transfer Learning Approach for Performance Optimization of Configurable Computer Systems
Md Shahriar Iqbal, Ziyuan Zhong, Iftakhar Ahmad +2
Modern computer systems are highly configurable, with hundreds of configuration options that interact, resulting in an enormous configuration space. As a result, optimizing perform…
Language-Guided Traffic Simulation via Scene-Level Diffusion
Ziyuan Zhong, Davis Rempe, Yuxiao Chen +5
Realistic and controllable traffic simulation is a core capability that is necessary to accelerate autonomous vehicle (AV) development. However, current approaches for controlling…
Guided Conditional Diffusion for Controllable Traffic Simulation
Ziyuan Zhong, Davis Rempe, Danfei Xu +5
Controllable and realistic traffic simulation is critical for developing and verifying autonomous vehicles. Typical heuristic-based traffic models offer flexible control to make ve…
Automatic Map Generation for Autonomous Driving System Testing
Yun Tang, Yuan Zhou, Kairui Yang +5
High-definition (HD) maps are essential in testing autonomous driving systems (ADSs). HD maps essentially determine the potential diversity of the testing scenarios. However, the c…
Repairing Group-Level Errors for DNNs Using Weighted Regularization
Ziyuan Zhong, Yuchi Tian, Conor J. Sweeney +2
Deep Neural Networks (DNNs) have been widely used in software making decisions impacting people's lives. However, they have been found to exhibit severe erroneous behaviors that ma…
A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation
Ziyuan Zhong, Yun Tang, Yuan Zhou +3
Automated Driving Systems (ADSs) have seen rapid progress in recent years. To ensure the safety and reliability of these systems, extensive testings are being conducted before thei…
Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems
Ziyuan Zhong, Zhisheng Hu, Shengjian Guo +3
Advanced Driver-Assistance Systems (ADAS) have been thriving and widely deployed in recent years. In general, these systems receive sensor data, compute driving decisions, and outp…
Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles
Ziyuan Zhong, Gail Kaiser, Baishakhi Ray
Self-driving cars and trucks, autonomous vehicles (AVs), should not be accepted by regulatory bodies and the public until they have much higher confidence in their safety and relia…
Understanding Local Robustness of Deep Neural Networks under Natural Variations
Ziyuan Zhong, Yuchi Tian, Baishakhi Ray
Deep Neural Networks (DNNs) are being deployed in a wide range of settings today, from safety-critical applications like autonomous driving to commercial applications involving ima…
Metric Learning for Adversarial Robustness
Chengzhi Mao, Ziyuan Zhong, Junfeng Yang +2
Deep networks are well-known to be fragile to adversarial attacks. We conduct an empirical analysis of deep representations under the state-of-the-art attack method called PGD, and…
Testing DNN Image Classifiers for Confusion & Bias Errors
Yuchi Tian, Ziyuan Zhong, Vicente Ordonez +2
Image classifiers are an important component of today's software, from consumer and business applications to safety-critical domains. The advent of Deep Neural Networks (DNNs) is t…
Noise-tolerant fair classification
Alexandre Louis Lamy, Ziyuan Zhong, Aditya Krishna Menon +1
Fairness-aware learning involves designing algorithms that do not discriminate with respect to some sensitive feature (e.g., race or gender). Existing work on the problem operates…
PhasePack: A Phase Retrieval Library
Rohan Chandra, Ziyuan Zhong, Justin Hontz +3
Phase retrieval deals with the estimation of complex-valued signals solely from the magnitudes of linear measurements. While there has been a recent explosion in the development of…
PhasePack User Guide
Rohan Chandra, Ziyuan Zhong, Justin Hontz +3
"Phase retrieval" refers to the recovery of signals from the magnitudes (and not the phases) of linear measurements. While there has been a recent explosion in development of phase…