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Yaoqing Yang

29 papers hereh-index 203.6k citations60 works total

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

author position
  • first author13
  • middle author15
  • last author1

Across the 29 of 29 papers where every author was matched, so the position is known.

fields
  • cs.IT10
  • cs.LG6
  • cs.CV5
  • cs.SI2
  • stat.ML2
  • cs.CL1
same name
  • Yaoqing Yang — 12 papers, h 6
  • Yaoqing Yang — 10 papers, h 4
  • Yaoqing Yang — 7 papers, h 3
  • Yaoqing Yang — 4 papers, h 2
  • Yaoqing Yang — 1 paper, h 18

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

activity
20142023
most citedDeep Unsupervised Learning of 3D Point Clouds via Graph Topology Inference and Filtering

73 citations · 317 across the 29 of their papers we have counts for

collaborators
Showing 2017Show all

4 papers · 1 filter

cs.CV2017★ 24 cited

FoldingNet: Point Cloud Auto-encoder via Deep Grid Deformation

Yaoqing Yang, Chen Feng, Yiru Shen +1

Recent deep networks that directly handle points in a point set, e.g., PointNet, have been state-of-the-art for supervised learning tasks on point clouds such as classification and…

cs.CV2017★ 44 cited

Mining Point Cloud Local Structures by Kernel Correlation and Graph Pooling

Yiru Shen, Chen Feng, Yaoqing Yang +1

Unlike on images, semantic learning on 3D point clouds using a deep network is challenging due to the naturally unordered data structure. Among existing works, PointNet has achieve…

eess.SP2017★ 4 cited

Fast Path Localization on Graphs via Multiscale Viterbi Decoding

Yaoqing Yang, Siheng Chen, Mohammad Ali Maddah-Ali +3

We consider a problem of localizing a path-signal that evolves over time on a graph. A path-signal can be viewed as the trajectory of a moving agent on a graph in several consecuti…

cs.IT2017★ 9 cited

Coding Method for Parallel Iterative Linear Solver

Yaoqing Yang, Pulkit Grover, Soummya Kar

Computationally intensive distributed and parallel computing is often bottlenecked by a small set of slow workers known as stragglers. In this paper, we utilize the emerging idea o…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.