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Jianfeng Lu

51 papers hereh-index 354.7k citations179 works total

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

author position
  • sole author1
  • first author14
  • middle author23
  • last author11

Across the 49 of 51 papers where every author was matched, so the position is known.

fields
  • math.NA13
  • math-ph7
  • math.AP6
  • stat.ML6
  • cs.LG4
  • physics.chem-ph4
same name
  • Jianfeng Lu — 20 papers, h 28
  • Jianfeng Lu — 14 papers, h 5
  • Jianfeng Lu — 11 papers
  • Jianfeng Lu — 10 papers, h 3
  • Jianfeng Lu — 6 papers, h 7
  • Jianfeng Lu — 6 papers, h 1

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
20122026
most citedCoordinate descent full configuration interaction

47 citations · 124 across the 15 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2021★ 12 cited

Neural Collapse with Cross-Entropy Loss

Jianfeng Lu, Stefan Steinerberger

We consider the variational problem of cross-entropy loss with n feature vectors on a unit hypersphere in Rd. We prove that when d≥n−1, the global minimum is…

cs.LG2020

Global optimality of softmax policy gradient with single hidden layer neural networks in the mean-field regime

Andrea Agazzi, Jianfeng Lu

We study the problem of policy optimization for infinite-horizon discounted Markov Decision Processes with softmax policy and nonlinear function approximation trained with policy g…

cs.LG2020

A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions

Yulong Lu, Jianfeng Lu

This paper studies the universal approximation property of deep neural networks for representing probability distributions. Given a target distribution π and a source distributio…

cs.LG2020

Solving high-dimensional eigenvalue problems using deep neural networks: A diffusion Monte Carlo like approach

Jiequn Han, Jianfeng Lu, Mo Zhou

We propose a new method to solve eigenvalue problems for linear and semilinear second order differential operators in high dimensions based on deep neural networks. The eigenvalue…

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