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Fuzhen Zhuang

60 papers hereh-index 5116.4k citations210 works total

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

author position
  • first author3
  • middle author51
  • last author3

Across the 57 of 60 papers where every author was matched, so the position is known.

fields
  • cs.LG20
  • cs.IR18
  • cs.CL11
  • cs.AI3
  • cs.CV3
  • cs.CR1
same name
  • Fuzhen Zhuang — 59 papers, h 7
  • Fuzhen Zhuang — 11 papers, h 6
  • Fuzhen Zhuang — 3 papers, h 15
  • Fuzhen Zhuang — 3 papers, h 17
  • Fuzhen Zhuang — 2 papers, h 3
  • Fuzhen Zhuang — 2 papers, h 4

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
20182026
most citedDeep Subdomain Adaptation Network for Image Classification

1.2k citations · 2.8k across the 60 of their papers we have counts for

collaborators
Showing 2019 · cs.LGShow all

4 papers · 2 filters

cs.LG2019★ 3 cited

Transfer Learning Toolkit: Primers and Benchmarks

Fuzhen Zhuang, Keyu Duan, Tongjia Guo +4

The transfer learning toolkit wraps the codes of 17 transfer learning models and provides integrated interfaces, allowing users to use those models by calling a simple function. It…

cs.LG2019★ 227 cited

A Comprehensive Survey on Transfer Learning

Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan +5

Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this wa…

cs.LG2019

Efficient and Adaptive Kernelization for Nonlinear Max-margin Multi-view Learning

Changying Du, Jia He, Changde Du +3

Existing multi-view learning methods based on kernel function either require the user to select and tune a single predefined kernel or have to compute and store many Gram matrices…

cs.LG2019

Learning beyond Predefined Label Space via Bayesian Nonparametric Topic Modelling

Changying Du, Fuzhen Zhuang, Jia He +2

In real world machine learning applications, testing data may contain some meaningful new categories that have not been seen in labeled training data. To simultaneously recognize n…

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