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Yiming Li

Tsinghua University

22 papers hereh-index 162.2k citations29 works total

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

author position
  • first author10
  • middle author7
  • last author1

Across the 18 of 22 papers where every author was matched, so the position is known.

fields
  • cs.CR11
  • cs.LG4
  • cs.CV3
  • cs.SD2
  • eess.AS2
affiliations
  • Tsinghua University
Homepage
same name
  • Yiming Li — 26 papers, h 24
  • Yiming Li — 21 papers, h 9
  • Yiming Li — 16 papers, h 17
  • Yiming Li — 15 papers, h 68
  • Yiming Li — 14 papers, h 4
  • Yiming Li — 14 papers, h 8

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
20192023
most citedBackdoor Defense via Decoupling the Training Process

42 citations · 150 across the 14 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.CR2019

Visual Privacy Protection via Mapping Distortion

Yiming Li, Peidong Liu, Yong Jiang +1

Privacy protection is an important research area, which is especially critical in this big data era. To a large extent, the privacy of visual classification data is mainly in the m…

cs.CV2019

Adversarial Defense via Local Flatness Regularization

Jia Xu, Yiming Li, Yong Jiang +1

Adversarial defense is a popular and important research area. Due to its intrinsic mechanism, one of the most straightforward and effective ways of defending attacks is to analyze…

cs.LG2019

t-k-means: A Robust and Stable k-means Variant

Yiming Li, Yang Zhang, Qingtao Tang +3

k-means algorithm is one of the most classical clustering methods, which has been widely and successfully used in signal processing. However, due to the thin-tailed property of t…

cs.LG2019

Rectified Decision Trees: Towards Interpretability, Compression and Empirical Soundness

Jiawang Bai, Yiming Li, Jiawei Li +2

How to obtain a model with good interpretability and performance has always been an important research topic. In this paper, we propose rectified decision trees (ReDT), a knowledge…

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