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L. Jing

18 papers hereh-index 344.2k citations149 works total

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

author position
  • first author1
  • middle author6
  • last author10

Across the 17 of 18 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • cs.CV4
  • cs.LG4
  • cs.SD1
same name
  • L. Jing — 15 papers, h 15
  • L. Jing — 6 papers, h 15
  • L. Jing — 6 papers, h 10
  • L. Jing — 6 papers, h 15
  • L. Jing — 2 papers, h 8
  • L. Jing — 2 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
20162023
most citedProbing BERT in Hyperbolic Spaces

19 citations · 73 across the 18 of their papers we have counts for

collaborators
Showing cs.LGShow all

4 papers · 1 filter

cs.LG2023★ 1 cited

Recognizable Information Bottleneck

Yilin Lyu, Xin Liu, Mingyang Song +4

Information Bottlenecks (IBs) learn representations that generalize to unseen data by information compression. However, existing IBs are practically unable to guarantee generalizat…

cs.LG2022

Strong Transferable Adversarial Attacks via Ensembled Asymptotically Normal Distribution Learning

Zhengwei Fang, Rui Wang, Tao Huang +1

Strong adversarial examples are crucial for evaluating and enhancing the robustness of deep neural networks. However, the performance of popular attacks is usually sensitive, for i…

cs.LG2019★ 9 cited

Hyperbolic Interaction Model For Hierarchical Multi-Label Classification

Boli Chen, Xin Huang, Lin Xiao +2

Different from the traditional classification tasks which assume mutual exclusion of labels, hierarchical multi-label classification (HMLC) aims to assign multiple labels to every…

cs.LG2019★ 10 cited

Label-aware Document Representation via Hybrid Attention for Extreme Multi-Label Text Classification

Xin Huang, Boli Chen, Lin Xiao +1

Extreme multi-label text classification (XMTC) aims at tagging a document with most relevant labels from an extremely large-scale label set. It is a challenging problem especially…

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