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researcher

Jian Li

19 papers hereh-index 121k citations25 works total

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

author position
  • first author3
  • middle author10
  • last author5

Across the 18 of 19 papers where every author was matched, so the position is known.

fields
  • cs.LG10
  • cs.DS3
  • cs.CL2
  • astro-ph.EP1
  • cs.DC1
  • cs.DM1
same name
  • Jian Li — 52 papers, h 17
  • Jian Li — 50 papers, h 28
  • Jian Li — 47 papers, h 19
  • Jian Li — 32 papers, h 13
  • Jian Li — 30 papers, h 31
  • Jian Li — 17 papers, h 10

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
20142022
most citedGradient Descent Maximizes the Margin of Homogeneous Neural Networks

102 citations · 295 across the 17 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.DS2019★ 2 cited

Algorithms and Adaptivity Gaps for Stochastic k-TSP

Haotian Jiang, Jian Li, Daogao Liu +1

Given a metric (V,d) and a root∈V, the classic $\textsf{$k$-TSP}$ problem is to find a tour originating at the root of minimum length that visits at lea…

cs.CL2019★ 58 cited

Optimizing Speech Recognition For The Edge

Yuan Shangguan, Jian Li, Qiao Liang +2

While most deployed speech recognition systems today still run on servers, we are in the midst of a transition towards deployments on edge devices. This leap to the edge is powered…

cs.LG2019★ 102 cited

Gradient Descent Maximizes the Margin of Homogeneous Neural Networks

Kaifeng Lyu, Jian Li

In this paper, we study the implicit regularization of the gradient descent algorithm in homogeneous neural networks, including fully-connected and convolutional neural networks wi…

cs.LG2019★ 30 cited

On Generalization Error Bounds of Noisy Gradient Methods for Non-Convex Learning

Jian Li, Xuanyuan Luo, Mingda Qiao

Generalization error (also known as the out-of-sample error) measures how well the hypothesis learned from training data generalizes to previously unseen data. Proving tight genera…

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