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researcher

Youcheng Sun

29 papers hereh-index 232.7k citations61 works total

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

author position
  • first author7
  • middle author18
  • last author2

Across the 27 of 29 papers where every author was matched, so the position is known.

fields
  • cs.LG12
  • cs.SE5
  • cs.CR4
  • cs.NE2
  • cs.OS2
  • cs.AI1
same name
  • Youcheng Sun — 11 papers, h 1
  • Youcheng Sun — 6 papers, h 3
  • Youcheng Sun — 6 papers, h 4
  • Youcheng Sun — 3 papers, h 1
  • Youcheng Sun — 2 papers, h 2
  • Youcheng Sun — 1 paper, 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
20132023
most citedTesting Deep Neural Networks

173 citations · 397 across the 28 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.LG2018★ 54 cited

A Survey of Safety and Trustworthiness of Deep Neural Networks: Verification, Testing, Adversarial Attack and Defence, and Interpretability

Xiaowei Huang, Daniel Kroening, Wenjie Ruan +5

In the past few years, significant progress has been made on deep neural networks (DNNs) in achieving human-level performance on several long-standing tasks. With the broader deplo…

cs.LG2018★ 32 cited

Concolic Testing for Deep Neural Networks

Youcheng Sun, Min Wu, Wenjie Ruan +3

Concolic testing combines program execution and symbolic analysis to explore the execution paths of a software program. This paper presents the first concolic testing approach for…

cs.LG2018★ 22 cited

Global Robustness Evaluation of Deep Neural Networks with Provable Guarantees for the L0​ Norm

Wenjie Ruan, Min Wu, Youcheng Sun +3

Deployment of deep neural networks (DNNs) in safety- or security-critical systems requires provable guarantees on their correct behaviour. A common requirement is robustness to adv…

cs.LG2018★ 173 cited

Testing Deep Neural Networks

Youcheng Sun, Xiaowei Huang, Daniel Kroening +3

Deep neural networks (DNNs) have a wide range of applications, and software employing them must be thoroughly tested, especially in safety-critical domains. However, traditional so…

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