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researcher

Shuiwang Ji

45 papers hereh-index 6622.5k citations213 works total

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

author position
  • middle author4
  • last author39

Across the 43 of 45 papers where every author was matched, so the position is known.

fields
  • cs.LG24
  • cs.CV7
  • cs.CL4
  • eess.IV3
  • cs.AI1
  • cs.CY1
same name
  • Shuiwang Ji — 19 papers, h 13
  • Shuiwang Ji — 10 papers, h 7
  • Shuiwang Ji — 7 papers
  • Shuiwang Ji — 6 papers, h 6
  • Shuiwang Ji — 6 papers, h 3
  • Shuiwang Ji — 5 papers, h 3

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
20172023
most citedTowards Deeper Graph Neural Networks

515 citations · 1.3k across the 27 of their papers we have counts for

collaborators
Showing cs.CLShow all

4 papers · 1 filter

cs.CL2022

Duality-Induced Regularizer for Semantic Matching Knowledge Graph Embeddings

Jie Wang, Zhanqiu Zhang, Zhihao Shi +3

Semantic matching models -- which assume that entities with similar semantics have similar embeddings -- have shown great power in knowledge graph embeddings (KGE). Many existing s…

cs.CL2021

Sent2Matrix: Folding Character Sequences in Serpentine Manifolds for Two-Dimensional Sentence

Hongyang Gao, Yi Liu, Xuan Zhang +1

We study text representation methods using deep models. Current methods, such as word-level embedding and character-level embedding schemes, treat texts as either a sequence of ato…

cs.CL2020★ 7 cited

iCapsNets: Towards Interpretable Capsule Networks for Text Classification

Zhengyang Wang, Xia Hu, Shuiwang Ji

Many text classification applications require models with satisfying performance as well as good interpretability. Traditional machine learning methods are easy to interpret but ha…

cs.CL2019★ 1 cited

On Attribution of Recurrent Neural Network Predictions via Additive Decomposition

Mengnan Du, Ninghao Liu, Fan Yang +2

RNN models have achieved the state-of-the-art performance in a wide range of text mining tasks. However, these models are often regarded as black-boxes and are criticized due to th…

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