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Lingfei Wu

110 papers hereh-index 458.7k citations185 works total

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

author position
  • first author22
  • middle author64
  • last author19

Across the 105 of 110 papers where every author was matched, so the position is known.

fields
  • cs.LG27
  • cs.CL26
  • cs.AI11
  • cs.IR9
  • physics.soc-ph8
  • cs.SE6
same name
  • Lingfei Wu — 6 papers, h 3
  • Lingfei Wu — 6 papers, h 4
  • Lingfei Wu — 4 papers, h 1
  • Lingfei Wu — 3 papers, h 3
  • Lingfei Wu — 2 papers, h 8
  • Lingfei Wu — 1 paper

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
20112023
most citedIterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings

217 citations · 2.1k across the 107 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CL2023

SkillQG: Learning to Generate Question for Reading Comprehension Assessment

Xiaoqiang Wang, Bang Liu, Siliang Tang +1

We present $\textbf{$\texttt{SkillQG}$}$: a question generation framework with controllable comprehension types for assessing and improving machine reading comprehension models. Ex…

cs.AI2023

Tram: A Token-level Retrieval-augmented Mechanism for Source Code Summarization

Tong Ye, Lingfei Wu, Tengfei Ma +5

Automatically generating human-readable text describing the functionality of a program is the intent of source code summarization. Although neural language models achieve significa…

cs.AI2023★ 2 cited

KGTrust: Evaluating Trustworthiness of SIoT via Knowledge Enhanced Graph Neural Networks

Zhizhi Yu, Di Jin, Cuiying Huo +5

Social Internet of Things (SIoT), a promising and emerging paradigm that injects the notion of social networking into smart objects (i.e., things), paving the way for the next gene…

cs.LG2023

Pruning Before Training May Improve Generalization, Provably

Hongru Yang, Yingbin Liang, Xiaojie Guo +2

It has been observed in practice that applying pruning-at-initialization methods to neural networks and training the sparsified networks can not only retain the testing performance…

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