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researcher

Jun Li

26 papers hereh-index 5110.7k citations427 works total

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

author position
  • sole author1
  • first author4
  • middle author14
  • last author7

Across the 26 of 26 papers where every author was matched, so the position is known.

fields
  • cs.CV5
  • quant-ph5
  • cs.LG4
  • cs.NI3
  • eess.SP3
  • stat.ML2
same name
  • Jun Li — 30 papers, h 27
  • Jun Li — 28 papers
  • Jun Li — 22 papers
  • Jun Li — 13 papers
  • Jun Li — 13 papers, h 19
  • Jun Li — 10 papers

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
20192022
most citedFederated Learning with Differential Privacy: Algorithms and Performance Analysis

88 citations · 176 across the 21 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2020★ 12 cited

RDP-GAN: A Rényi-Differential Privacy based Generative Adversarial Network

Chuan Ma, Jun Li, Ming Ding +4

Generative adversarial network (GAN) has attracted increasing attention recently owing to its impressive ability to generate realistic samples with high privacy protection. Without…

cs.LG2020★ 29 cited

Efficient Riemannian Optimization on the Stiefel Manifold via the Cayley Transform

Jun Li, Li Fuxin, Sinisa Todorovic

Strictly enforcing orthonormality constraints on parameter matrices has been shown advantageous in deep learning. This amounts to Riemannian optimization on the Stiefel manifold, w…

cs.LG2019

Lifelong Spectral Clustering

Gan Sun, Yang Cong, Qianqian Wang +2

In the past decades, spectral clustering (SC) has become one of the most effective clustering algorithms. However, most previous studies focus on spectral clustering tasks with a f…

cs.LG2019★ 88 cited

Federated Learning with Differential Privacy: Algorithms and Performance Analysis

Kang Wei, Jun Li, Ming Ding +6

In this paper, to effectively prevent information leakage, we propose a novel framework based on the concept of differential privacy (DP), in which artificial noises are added to t…

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