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researcher

Yuke Wang

8 papers hereh-index 424 citations10 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4
  • last author3

Across the 7 of 8 papers where every author was matched, so the position is known.

fields
  • cs.DC4
  • cs.CV2
  • cs.LG1
  • q-bio.QM1
same name
  • Yuke Wang — 4 papers, h 2
  • Yuke Wang — 3 papers, h 5
  • Yuke Wang — 2 papers, h 2
  • Yuke Wang — 2 papers, h 1
  • Yuke Wang — 1 paper, h 1
  • Yuke Wang — 1 paper, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

collaborators
Showing cs.DCShow all

4 papers · 1 filter

cs.DC2026

Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication

Wenqi Jia, Zhewen Hu, Ying Huang +10

3D Gaussian Splatting (3DGS) enables high-fidelity and real-time 3D scene reconstruction, but scaling training to large-scale scenes requires optimizing hundreds of millions of Gau…

cs.DC2026

GENSERVE: Efficient Co-Serving of Heterogeneous Diffusion Model Workloads

Fanjiang Ye, Zhangke Li, Xinrui Zhong +10

Diffusion models have emerged as the prevailing approach for text-to-image (T2I) and text-to-video (T2V) generation, yet production platforms must increasingly serve both modalitie…

cs.DC2025

An Efficient and Adaptive Watermark Detection System with Tile-based Error Correction

Xinrui Zhong, Xinze Feng, Jingwei Zuo +6

Efficient and reliable detection of generated images is critical for the responsible deployment of generative models. Existing approaches primarily focus on improving detection acc…

cs.DC2025

PATCHEDSERVE: A Patch Management Framework for SLO-Optimized Hybrid Resolution Diffusion Serving

Desen Sun, Zepeng Zhao, Yuke Wang

The Text-to-Image (T2I) diffusion model has emerged as one of the most widely adopted generative models. However, serving diffusion models at the granularity of entire images intro…

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