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Hui Jiang

20 papers hereh-index 358.7k citations158 works total

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

author position
  • sole author1
  • first author1
  • middle author6
  • last author10

Across the 18 of 20 papers where every author was matched, so the position is known.

fields
  • cs.CL10
  • cs.LG4
  • cs.CV2
  • cs.NE2
  • cs.AI1
  • cs.IR1
same name
  • Hui Jiang — 5 papers, h 9
  • Hui Jiang — 5 papers, h 7
  • Hui Jiang — 4 papers
  • Hui Jiang — 4 papers, h 2
  • Hui Jiang — 3 papers
  • Hui Jiang — 3 papers, h 5

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
20152021
most citedExploring Question Understanding and Adaptation in Neural-Network-Based Question Answering

38 citations · 134 across the 12 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.LG2018

DropFilter: A Novel Regularization Method for Learning Convolutional Neural Networks

Hengyue Pan, Hui Jiang, Xin Niu +1

The past few years have witnessed the fast development of different regularization methods for deep learning models such as fully-connected deep neural networks (DNNs) and Convolut…

cs.AI2018

Explicit Utilization of General Knowledge in Machine Reading Comprehension

Chao Wang, Hui Jiang

To bridge the gap between Machine Reading Comprehension (MRC) models and human beings, which is mainly reflected in the hunger for data and the robustness to noise, in this paper,…

cs.CL2018

The Lower The Simpler: Simplifying Hierarchical Recurrent Models

Chao Wang, Hui Jiang

To improve the training efficiency of hierarchical recurrent models without compromising their performance, we propose a strategy named as `the lower the simpler', which is to simp…

cs.CL2018

Using Neural Network for Identifying Clickbaits in Online News Media

Amin Omidvar, Hui Jiang, Aijun An

Online news media sometimes use misleading headlines to lure users to open the news article. These catchy headlines that attract users but disappointed them at the end, are called…

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