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Fan Wu

18 papers hereh-index 11368 citations21 works total

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

author position
  • first author7
  • middle author9
  • last author2

Across the 18 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CV6
  • cs.LG3
  • stat.ML3
  • cs.GT2
  • cs.IR1
  • cs.SI1
same name
  • Fan Wu — 28 papers, h 8
  • Fan Wu — 26 papers, h 43
  • Fan Wu — 24 papers, h 12
  • Fan Wu — 19 papers, h 4
  • Fan Wu — 18 papers, h 3
  • Fan Wu — 16 papers, h 7

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
20132022
most citedDenseBody: Directly Regressing Dense 3D Human Pose and Shape From a Single Color Image

30 citations · 123 across the 18 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.GT2021★ 1 cited

On Designing a Two-stage Auction for Online Advertising

Yiqing Wang, Xiangyu Liu, Zhenzhe Zheng +4

For the scalability of industrial online advertising systems, a two-stage auction architecture is widely used to enable efficient ad allocation on a large set of corpus within a li…

cs.CV2021★ 2 cited

Normal Learning in Videos with Attention Prototype Network

Chao Hu, Fan Wu, Weijie Wu +2

Frame reconstruction (current or future frame) based on Auto-Encoder (AE) is a popular method for video anomaly detection. With models trained on the normal data, the reconstructio…

eess.SP2021★ 2 cited

Nearly Minimax-Optimal Rates for Noisy Sparse Phase Retrieval via Early-Stopped Mirror Descent

Fan Wu, Patrick Rebeschini

This paper studies early-stopped mirror descent applied to noisy sparse phase retrieval, which is the problem of recovering a k-sparse signal x⋆∈Rn fr…

stat.ML2021★ 1 cited

Implicit Regularization in Matrix Sensing via Mirror Descent

Fan Wu, Patrick Rebeschini

We study discrete-time mirror descent applied to the unregularized empirical risk in matrix sensing. In both the general case of rectangular matrices and the particular case of pos…

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