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Fuyuan Zhang

15 papers hereh-index 141.7k citations37 works total

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

author position
  • first author1
  • middle author10
  • last author3

Across the 14 of 15 papers where every author was matched, so the position is known.

fields
  • cs.SE11
  • cs.CR1
  • cs.FL1
  • cs.LG1
  • cs.PL1
same name
  • Fuyuan Zhang — 6 papers, h 4
  • Fuyuan Zhang — 1 paper
  • Fuyuan Zhang — 1 paper, h 2

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
20152026
most citedDeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

591 citations · 697 across the 15 of their papers we have counts for

collaborators
Showing 2018Show all

4 papers · 1 filter

cs.SE2018

An Event-based Compositional Reasoning Approach for Concurrent Reactive Systems

Yongwang Zhao, David Sanan, Fuyuan Zhang +1

Reactive systems are composed of a well defined set of input events that the system reacts with by executing an associated handler to each event. In concurrent environments, event…

cs.SE2018★ 58 cited

Combinatorial Testing for Deep Learning Systems

Lei Ma, Fuyuan Zhang, Minhui Xue +4

Deep learning (DL) has achieved remarkable progress over the past decade and been widely applied to many safety-critical applications. However, the robustness of DL systems recentl…

cs.SE2018★ 29 cited

DeepMutation: Mutation Testing of Deep Learning Systems

Lei Ma, Fuyuan Zhang, Jiyuan Sun +8

Deep learning (DL) defines a new data-driven programming paradigm where the internal system logic is largely shaped by the training data. The standard way of evaluating DL models i…

cs.SE2018★ 591 cited

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

Lei Ma, Felix Juefei-Xu, Fuyuan Zhang +9

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data. We have seen…

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