16 citations · 29 across the 4 of their papers we have counts for
5 papers
SteerQuant: Steering Quantization Error with Action-Guided Scaling in World-Action Models
Yunhan Wang, Haodong Wang, Zhiming Liu +7
World-action models (WAMs) jointly generate future world states and actions through iterative denoising, using shared weights to process heterogeneous semantic streams of video, pr…
MosaicQuant: Inlier-Outlier Disaggregation for Unified 4-Bit LLM Quantization
Yangjia Hu, Haodong Wang, Zicong Hong +8
4-bit quantization significantly reduces the memory footprint and accelerates the inference of large language models (LLMs). However, its limited bit-width representation struggles…
Pipeline Parallelism for Inference on Heterogeneous Edge Computing
Yang Hu, Connor Imes, Xuanang Zhao +4
Deep neural networks with large model sizes achieve state-of-the-art results for tasks in computer vision (CV) and natural language processing (NLP). However, these large-scale mod…
Tackling Variabilities in Autonomous Driving
Yuqiong Qi, Yang Hu, Haibin Wu +5
The state-of-the-art driving automation system demands extreme computational resources to meet rigorous accuracy and latency requirements. Though emerging driving automation comput…
Solution for Large-Scale Hierarchical Object Detection Datasets with Incomplete Annotation and Data Imbalance
Yuan Gao, Xingyuan Bu, Yang Hu +4
This report demonstrates our solution for the Open Images 2018 Challenge. Based on our detailed analysis on the Open Images Datasets (OID), it is found that there are four typical…