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Pengfei Li

Nanyang Technological University, Singapore

4 papers hereh-index 458.1k citations427 works total

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

author position
  • first author1
  • middle author2
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cond-mat.str-el2
  • cond-mat.mes-hall1
  • cs.CL1
affiliations
  • Nanyang Technological University, Singapore
same name
  • Pengfei Li — 12 papers, h 12
  • Pengfei Li — 11 papers, h 14
  • Pengfei Li — 6 papers, h 11
  • Pengfei Li — 3 papers
  • Pengfei Li — 3 papers
  • Pengfei Li — 2 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

most citedImproving Relation Extraction with Knowledge-attention

27 citations · 27 across the 2 of their papers we have counts for

collaborators

4 papers

cond-mat.str-el2021

Quantum cluster kink and ring frustration

Zhen-Yu Zheng, Han-Chuan Kou, Peng Li

In this paper, we work on the pure and mixed cluster models with periodic boundary condition. The first purpose is to establish the concept of quantum cluster kink. We clarify that…

cond-mat.mes-hall2021

Roadmap of spin-orbit torques

Qiming Shao, Peng Li, Luqiao Liu +16

Spin-orbit torque (SOT) is an emerging technology that enables the efficient manipulation of spintronic devices. The initial processes of interest in SOTs involved electric fields,…

cond-mat.str-el2021

Revealing the Heavy Quasiparticles in the Heavy-Fermion Superconductor CeCu2Si2

Zhongzheng Wu, Yuan Fang, Hang Su +11

The superconducting order parameter of the first heavy-fermion superconductor CeCu2Si2 is currently under debate. A key ingredient to understand its superconductivity and physical…

cs.CL2019★ 27 cited

Improving Relation Extraction with Knowledge-attention

Pengfei Li, Kezhi Mao, Xuefeng Yang +1

While attention mechanisms have been proven to be effective in many NLP tasks, majority of them are data-driven. We propose a novel knowledge-attention encoder which incorporates p…

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