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Jun Wu

4 papers hereh-index 6181 citations10 works total

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

author position
  • first author2
  • middle author1
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.AI1
  • cs.LG1
  • cs.SI1
  • math.ST1
same name
  • Jun Wu — 12 papers, h 12
  • Jun Wu — 12 papers, h 5
  • Jun Wu — 10 papers, h 3
  • Jun Wu — 9 papers, h 24
  • Jun Wu — 8 papers, h 24
  • Jun Wu — 8 papers, h 12

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

most citedMedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression

10 citations · 18 across the 4 of their papers we have counts for

collaborators

4 papers

cs.SI2023★ 3 cited

FakeSwarm: Improving Fake News Detection with Swarming Characteristics

Jun Wu, Xuesong Ye

The proliferation of fake news poses a serious threat to society, as it can misinform and manipulate the public, erode trust in institutions, and undermine democratic processes. To…

cs.AI2023★ 1 cited

FineEHR: Refine Clinical Note Representations to Improve Mortality Prediction

Jun Wu, Xuesong Ye, Chengjie Mou +1

Monitoring the health status of patients in the Intensive Care Unit (ICU) is a critical aspect of providing superior care and treatment. The availability of large-scale electronic…

cs.LG2023★ 10 cited

MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression

Xuesong Ye, Jun Wu, Chengjie Mou +1

Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment. Massive medical signs in electronic hea…

math.ST2020★ 4 cited

Structure Learning for Cyclic Linear Causal Models

Carlos Améndola, Philipp Dettling, Mathias Drton +2

We consider the problem of structure learning for linear causal models based on observational data. We treat models given by possibly cyclic mixed graphs, which allow for feedback…

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