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Guy Katz

25 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 author2
  • middle author9
  • last author12

Across the 25 of 25 papers where every author was matched, so the position is known.

fields
  • cs.LG10
  • cs.LO7
  • cs.SE4
  • cs.AI3
  • cs.FL1
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
  • Guy Katz — 3 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 · 128 across the 12 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.LG2021

Minimal Multi-Layer Modifications of Deep Neural Networks

Idan Refaeli, Guy Katz

Deep neural networks (DNNs) have become increasingly popular in recent years. However, despite their many successes, DNNs may also err and produce incorrect and potentially fatal o…

cs.LG2021

Pruning and Slicing Neural Networks using Formal Verification

Ori Lahav, Guy Katz

Deep neural networks (DNNs) play an increasingly important role in various computer systems. In order to create these networks, engineers typically specify a desired topology, and…

cs.LG2021

Towards Scalable Verification of Deep Reinforcement Learning

Guy Amir, Michael Schapira, Guy Katz

Deep neural networks (DNNs) have gained significant popularity in recent years, becoming the state of the art in a variety of domains. In particular, deep reinforcement learning (D…

cs.SE2021

Towards Repairing Scenario-Based Models with Rich Events

Guy Katz

Repairing legacy systems is a difficult and error-prone task: often, limited knowledge of the intricacies of these systems could make an attempted repair result in new errors. Cons…

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