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Chenguang Wang

UCSC

14 papers hereh-index 201.6k citations31 works total

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

author position
  • sole author1
  • first author8
  • middle author4

Across the 13 of 14 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.LG4
  • cs.SI1
affiliations
  • UCSC
HomepageORCID 0009-0006-9545-2844
same name
  • Chenguang Wang — 43 papers, h 13
  • Chenguang Wang — 12 papers, h 2
  • Chenguang Wang — 10 papers, h 6
  • Chenguang Wang — 8 papers, h 5
  • Chenguang Wang — 7 papers, h 6
  • Chenguang Wang — 3 papers, h 18

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
20162022
most citedGluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

208 citations · 370 across the 13 of their papers we have counts for

collaborators
Showing 2019Show all

4 papers · 1 filter

cs.SI2019★ 1 cited

Meta-Path Constrained Random Walk Inference for Large-Scale Heterogeneous Information Networks

Chenguang Wang

Heterogeneous information network (HIN) has shown its power of modeling real world data as a multi-typed entity-relation graph. Meta-path is the key contributor to this power since…

cs.CL2019★ 2 cited

PoD: Positional Dependency-Based Word Embedding for Aspect Term Extraction

Yichun Yin, Chenguang Wang, Ming Zhang

Dependency context-based word embedding jointly learns the representations of word and dependency context, and has been proved effective in aspect term extraction. In this paper, w…

cs.LG2019★ 208 cited

GluonCV and GluonNLP: Deep Learning in Computer Vision and Natural Language Processing

Jian Guo, He He, Tong He +13

We present GluonCV and GluonNLP, the deep learning toolkits for computer vision and natural language processing based on Apache MXNet (incubating). These toolkits provide state-of-…

cs.CL2019★ 46 cited

Language Models with Transformers

Chenguang Wang, Mu Li, Alexander J. Smola

The Transformer architecture is superior to RNN-based models in computational efficiency. Recently, GPT and BERT demonstrate the efficacy of Transformer models on various NLP tasks…

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