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researcher

Jianxin Li

35 papers hereh-index 2811k citations71 works total

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

author position
  • first author2
  • middle author21
  • last author8

Across the 31 of 35 papers where every author was matched, so the position is known.

fields
  • cs.LG12
  • cs.CL8
  • cs.SI8
  • cs.AI2
  • cs.CY1
  • cs.DL1
same name
  • Jianxin Li — 39 papers, h 36
  • Jianxin Li — 30 papers, h 10
  • Jianxin Li — 11 papers, h 2
  • Jianxin Li — 10 papers, h 5
  • Jianxin Li — 10 papers, h 5
  • Jianxin Li — 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
20172026
most citedInformer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting

476 citations · 930 across the 34 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.LG2018★ 8 cited

Graph Convolutional Neural Networks via Motif-based Attention

Hao Peng, Jianxin Li, Qiran Gong +3

Many real-world problems can be represented as graph-based learning problems. In this paper, we propose a novel framework for learning spatial and attentional convolution neural ne…

cs.AI2018★ 8 cited

Modeling relation paths for knowledge base completion via joint adversarial training

Chen Li, Xutan Peng, Shanghang Zhang +5

Knowledge Base Completion (KBC), which aims at determining the missing relations between entity pairs, has received increasing attention in recent years. Most existing KBC methods…

cs.SD2018

A Lightweight Music Texture Transfer System

Xutan Peng, Chen Li, Zhi Cai +3

Deep learning researches on the transformation problems for image and text have raised great attention. However, present methods for music feature transfer using neural networks ar…

cs.SI2018★ 2 cited

Inferring Multiplex Diffusion Network via Multivariate Marked Hawkes Process

Peiyuan Suny, Jianxin Li, Yongyi Mao +2

Understanding the diffusion in social network is an important task. However, this task is challenging since (1) the network structure is usually hidden with only observations of ev…

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