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researcher

Guy Katz

43 papers hereh-index 295.3k citations85 works total

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

author position
  • sole author2
  • first author3
  • middle author16
  • last author22

Across the 43 of 43 papers where every author was matched, so the position is known.

fields
  • cs.LG21
  • cs.LO8
  • cs.SE5
  • cs.AI4
  • cs.NE2
  • cs.RO2
same name
  • Guy Katz — 17 papers, h 9
  • Guy Katz — 12 papers, h 7
  • Guy Katz — 5 papers, h 2
  • Guy Katz — 4 papers, h 2
  • Guy Katz — 3 papers, h 1
  • Guy Katz — 2 papers, h 1

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 citedTowards Proving the Adversarial Robustness of Deep Neural Networks

119 citations · 468 across the 39 of their papers we have counts for

collaborators
Showing cs.AIShow all

4 papers · 1 filter

cs.AI2023★ 1 cited

Formally Explaining Neural Networks within Reactive Systems

Shahaf Bassan, Guy Amir, Davide Corsi +2

Deep neural networks (DNNs) are increasingly being used as controllers in reactive systems. However, DNNs are highly opaque, which renders it difficult to explain and justify their…

cs.AI2020

Verifying Recurrent Neural Networks using Invariant Inference

Yuval Jacoby, Clark Barrett, Guy Katz

Deep neural networks are revolutionizing the way complex systems are developed. However, these automatically-generated networks are opaque to humans, making it difficult to reason…

cs.AI2018★ 19 cited

Toward Scalable Verification for Safety-Critical Deep Networks

Lindsey Kuper, Guy Katz, Justin Gottschlich +3

The increasing use of deep neural networks for safety-critical applications, such as autonomous driving and flight control, raises concerns about their safety and reliability. Form…

cs.AI2017★ 2 cited

Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks

Guy Katz, Clark Barrett, David Dill +2

Deep neural networks have emerged as a widely used and effective means for tackling complex, real-world problems. However, a major obstacle in applying them to safety-critical syst…

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