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researcher

S. Sun

20 papers hereh-index 244.2k citations98 works total

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

author position
  • sole author1
  • first author3
  • middle author10
  • last author4

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

fields
  • cs.CL9
  • q-bio.QM4
  • q-bio.BM2
  • cs.DS1
  • cs.LG1
  • cs.RO1
same name
  • S. Sun — 297 papers, h 54
  • S. Sun — 263 papers
  • S. Sun — 168 papers
  • S. Sun — 54 papers
  • S. Sun — 30 papers, h 5
  • S. Sun — 29 papers

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
20152023
most citedAccurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

1k citations · 1.7k across the 19 of their papers we have counts for

collaborators
Showing 2015Show all

4 papers · 1 filter

q-bio.QM2015

Predicting diverse M-best protein contact maps

Siqi Sun, Jianzhu Ma, Sheng Wang +1

Protein contacts contain important information for protein structure and functional study, but contact prediction from sequence information remains very challenging. Recently evolu…

stat.ML2015★ 9 cited

AUC-maximized Deep Convolutional Neural Fields for Sequence Labeling

Sheng Wang, Siqi Sun, Jinbo Xu

Deep Convolutional Neural Networks (DCNN) has shown excellent performance in a variety of machine learning tasks. This manuscript presents Deep Convolutional Neural Fields (DeepCNF…

stat.ME2015

Learning Nonparametric Forest Graphical Models with Prior Information

Yuancheng Zhu, Zhe Liu, Siqi Sun

We present a framework for incorporating prior information into nonparametric estimation of graphical models. To avoid distributional assumptions, we restrict the graph to be a for…

cs.LG2015★ 13 cited

Learning Scale-Free Networks by Dynamic Node-Specific Degree Prior

Qingming Tang, Siqi Sun, Jinbo Xu

Learning the network structure underlying data is an important problem in machine learning. This paper introduces a novel prior to study the inference of scale-free networks, which…

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