ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2408.06212
  4. Cited By
Computability of Classification and Deep Learning: From Theoretical
  Limits to Practical Feasibility through Quantization

Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization

12 August 2024
Holger Boche
Vít Fojtík
Adalbert Fono
Gitta Kutyniok
ArXivPDFHTML

Papers citing "Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization"

2 / 2 papers shown
Title
Learning ReLU networks to high uniform accuracy is intractable
Learning ReLU networks to high uniform accuracy is intractable
Julius Berner
Philipp Grohs
F. Voigtlaender
19
4
0
26 May 2022
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networks
Guy Katz
Clark W. Barrett
D. Dill
Kyle D. Julian
Mykel Kochenderfer
AAML
219
1,818
0
03 Feb 2017
1