39 citations · 230 across the 69 of their papers we have counts for
13 papers · 1 filter
On Variance Reduction in Learning Mean Flows
Juanwu Lu, Ziran Wang
One-step generative modeling has emerged as a leading approach for amortizing the inference cost of diffusion and flow-matching models. Among distillation-free methods, MeanFlow tr…
VISTA-DZ: Visual Semantic Trajectory Adaptation for Personalized Dilemma Zone Prediction
Chuheng Wei, Ziye Qin, Ziran Wang +1
Driver decision making in the dilemma zone at signalized intersections is safety critical, as vehicles approaching a yellow signal must decide whether to stop or proceed within lim…
Analytical Correction for Subsampling Bias in Drifting Models
Jiaru Zhang, Zeyun Deng, Juanwu Lu +2
Drifting models are capable one-step generative models trained to follow a drifting field. The field combines attractive and repulsive softmax-weighted centroids over the data and…
Mollified Value Learning
Hrishikesh Viswanath, Juanwu Lu, S. Talha Bukhari +4
Offline goal-conditioned reinforcement learning (GCRL) learns goal-reaching behaviors from static datasets, but accurate value estimation remains challenging under limited state-ac…
A Universal and Robust Framework for Multiple Gas Recognition Based-on Spherical Normalization-Coupled Mahalanobis Algorithm
Shuai Chen, Yang Song, Chen Wang +1
Electronic nose (E-nose) systems face two interconnected challenges in open-set gas recognition: feature distribution shift caused by signal drift and decision boundary failure ind…
Accelerating Inference of Discrete Autoregressive Normalizing Flows by Selective Jacobi Decoding
Jiaru Zhang, Juanwu Lu, Xiaoyu Wu +2
Discrete normalizing flows are promising generative models with advantages such as analytical log-likelihood computation and end-to-end training. However, the architectural constra…
Digital Ethics in Federated Learning
Liangqi Yuan, Ziran Wang, Christopher G. Brinton
The Internet of Things (IoT) consistently generates vast amounts of data, sparking increasing concern over the protection of data privacy and the limitation of data misuse. Federat…
Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges
Vishnu Pandi Chellapandi, Liangqi Yuan, Christopher G. Brinton +2
Machine learning (ML) is widely used for key tasks in Connected and Automated Vehicles (CAV), including perception, planning, and control. However, its reliance on vehicular data f…
Decentralized Federated Learning: A Survey and Perspective
Liangqi Yuan, Ziran Wang, Lichao Sun +2
Federated learning (FL) has been gaining attention for its ability to share knowledge while maintaining user data, protecting privacy, increasing learning efficiency, and reducing…
Peer-to-Peer Federated Continual Learning for Naturalistic Driving Action Recognition
Liangqi Yuan, Yunsheng Ma, Lu Su +1
Naturalistic driving action recognition (NDAR) has proven to be an effective method for detecting driver distraction and reducing the risk of traffic accidents. However, the intrus…
A Survey of Federated Learning for Connected and Automated Vehicles
Vishnu Pandi Chellapandi, Liangqi Yuan, Stanislaw H /. Zak +1
Connected and Automated Vehicles (CAVs) are one of the emerging technologies in the automotive domain that has the potential to alleviate the issues of accidents, traffic congestio…
Federated Transfer-Ordered-Personalized Learning for Driver Monitoring Application
Liangqi Yuan, Lu Su, Ziran Wang
Federated learning (FL) shines through in the internet of things (IoT) with its ability to realize collaborative learning and improve learning efficiency by sharing client model pa…
Long-Term Prediction of Lane Change Maneuver Through a Multilayer Perceptron
Zhenyu Shou, Ziran Wang, Kyungtae Han +3
Behavior prediction plays an essential role in both autonomous driving systems and Advanced Driver Assistance Systems (ADAS), since it enhances vehicle's awareness of the imminent…