70 citations · 205 across the 31 of their papers we have counts for
32 papers
Accelerating MoE Training through Expert Placement and Replica Coordination
Zhiqiang Tan, Maoxin Wang, Wenxiang Lin +4
Distributed training of sparsely activated mixture-of-experts (MoE) models is often bottlenecked by workload imbalance and all-to-all (A2A) communication induced by dynamic token r…
Laplacian Frequency Hierarchies for Efficient 3D Gaussian Splatting Training
Yixiong Yang, Sisheng Zhang, Qingsong Yan +2
A key bottleneck in 3D Gaussian Splatting training is the continual growth of Gaussian primitives, which increases optimization cost and slows convergence, especially at high resol…
Bidirectional Resource Scheduling for Disaggregated and Asynchronous RL Post-Training
Zhiqiang Tan, Maoxin Wang, Sijie Wang +4
It is well established that the reasoning capabilities of large language models (LLMs) can be improved by applying reinforcement learning (RL) in a post-training stage. In a standa…
Position: LLM Inference Should Be Evaluated as Energy-to-Token Production
Xiang Liu, Shimiao Yuan, Zhenheng Tang +5
LLM inference is still evaluated mainly as a model or software problem: accuracy, latency, throughput, and hardware utilization. This is incomplete. At deployment scale, the releva…
RA-NeRF: Robust Neural Radiance Field Reconstruction with Accurate Camera Pose Estimation under Complex Trajectories
Qingsong Yan, Qiang Wang, Kaiyong Zhao +4
Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) have emerged as powerful tools for 3D reconstruction and SLAM tasks. However, their performance depends heavily on ac…
SphereFusion: Efficient Panorama Depth Estimation via Gated Fusion
Qingsong Yan, Qiang Wang, Kaiyong Zhao +4
Due to the rapid development of panorama cameras, the task of estimating panorama depth has attracted significant attention from the computer vision community, especially in applic…