1.7k citations · 2.4k across the 35 of their papers we have counts for
35 papers
EnsembleFollower: A Hybrid Car-Following Framework Based On Reinforcement Learning and Hierarchical Planning
Xu Han, Xianda Chen, Meixin Zhu +3
Car-following models have made significant contributions to our understanding of longitudinal driving behavior. However, they often exhibit limited accuracy and flexibility, as the…
LoRA-FA: Efficient and Effective Low Rank Representation Fine-tuning
Longteng Zhang, Lin Zhang, Shaohuai Shi +2
Fine-tuning large language models (LLMs) is crucial for improving their performance on downstream tasks, but full-parameter fine-tuning (Full-FT) is computationally expensive and m…
Evaluation and Optimization of Gradient Compression for Distributed Deep Learning
Lin Zhang, Longteng Zhang, Shaohuai Shi +2
To accelerate distributed training, many gradient compression methods have been proposed to alleviate the communication bottleneck in synchronous stochastic gradient descent (S-SGD…
FedML Parrot: A Scalable Federated Learning System via Heterogeneity-aware Scheduling on Sequential and Hierarchical Training
Zhenheng Tang, Xiaowen Chu, Ryan Yide Ran +7
Federated Learning (FL) enables collaborations among clients for train machine learning models while protecting their data privacy. Existing FL simulation platforms that are design…
DeAR: Accelerating Distributed Deep Learning with Fine-Grained All-Reduce Pipelining
Lin Zhang, Shaohuai Shi, Xiaowen Chu +3
Communication scheduling has been shown to be effective in accelerating distributed training, which enables all-reduce communications to be overlapped with backpropagation computat…
Rethinking Disparity: A Depth Range Free Multi-View Stereo Based on Disparity
Qingsong Yan, Qiang Wang, Kaiyong Zhao +3
Existing learning-based multi-view stereo (MVS) methods rely on the depth range to build the 3D cost volume and may fail when the range is too large or unreliable. To address this…