Zobrazeno 1 - 10
of 46
pro vyhledávání: '"KOKKE, WEN"'
Autor:
Atkey, Robert, Kokke, Wen
Publikováno v:
Electronic Notes in Theoretical Informatics and Computer Science, Volume 4 - Proceedings of MFPS XL (December 11, 2024) entics:14870
Multiplicative-Additive System Virtual (MAV) is a logic that extends Multiplicative-Additive Linear Logic with a self-dual non-commutative operator expressing the concept of "before" or "sequencing". MAV is also an extenson of the the logic Basic Sys
Externí odkaz:
http://arxiv.org/abs/2404.06233
Autor:
Wu, Haoze, Isac, Omri, Zeljić, Aleksandar, Tagomori, Teruhiro, Daggitt, Matthew, Kokke, Wen, Refaeli, Idan, Amir, Guy, Julian, Kyle, Bassan, Shahaf, Huang, Pei, Lahav, Ori, Wu, Min, Zhang, Min, Komendantskaya, Ekaterina, Katz, Guy, Barrett, Clark
This paper serves as a comprehensive system description of version 2.0 of the Marabou framework for formal analysis of neural networks. We discuss the tool's architectural design and highlight the major features and components introduced since its in
Externí odkaz:
http://arxiv.org/abs/2401.14461
Recent work has described the presence of the embedding gap in neural network verification. On one side of the gap is a high-level specification about the network's behaviour, written by a domain expert in terms of the interpretable problem space. On
Externí odkaz:
http://arxiv.org/abs/2402.01353
Autor:
Daggitt, Matthew L., Kokke, Wen, Atkey, Robert, Slusarz, Natalia, Arnaboldi, Luca, Komendantskaya, Ekaterina
Neuro-symbolic programs -- programs containing both machine learning components and traditional symbolic code -- are becoming increasingly widespread. However, we believe that there is still a lack of a general methodology for verifying these program
Externí odkaz:
http://arxiv.org/abs/2401.06379
Autor:
Casadio, Marco, Komendantskaya, Ekaterina, Rieser, Verena, Daggitt, Matthew L., Kienitz, Daniel, Arnaboldi, Luca, Kokke, Wen
With the proliferation of Deep Machine Learning into real-life applications, a particular property of this technology has been brought to attention: robustness Neural Networks notoriously present low robustness and can be highly sensitive to small in
Externí odkaz:
http://arxiv.org/abs/2206.14575
Verification of neural networks is currently a hot topic in automated theorem proving. Progress has been rapid and there are now a wide range of tools available that can verify properties of networks with hundreds of thousands of nodes. In theory thi
Externí odkaz:
http://arxiv.org/abs/2202.05207
Publikováno v:
Logical Methods in Computer Science, Volume 19, Issue 3 (July 12, 2023) lmcs:9361
This paper introduces Hypersequent GV (HGV), a modular and extensible core calculus for functional programming with session types that enjoys deadlock freedom, confluence, and strong normalisation. HGV exploits hyper-environments, which are collectio
Externí odkaz:
http://arxiv.org/abs/2105.08996
Autor:
Casadio, Marco, Komendantskaya, Ekaterina, Daggitt, Matthew L., Kokke, Wen, Katz, Guy, Amir, Guy, Refaeli, Idan
Neural networks are very successful at detecting patterns in noisy data, and have become the technology of choice in many fields. However, their usefulness is hampered by their susceptibility to adversarial attacks. Recently, many methods for measuri
Externí odkaz:
http://arxiv.org/abs/2104.01396
Autor:
Kokke, Wen, Dardha, Ornela
Priority Sesh is a library for session-typed communication in Linear Haskell which offers strong compile-time correctness guarantees. Priority Sesh offers two deadlock-free APIs for session-typed communication. The first guarantees deadlock freedom b
Externí odkaz:
http://arxiv.org/abs/2103.14481
Autor:
Kokke, Wen, Dardha, Ornela
Publikováno v:
Logical Methods in Computer Science, Volume 19, Issue 4 (December 18, 2023) lmcs:8867
Binary session types guarantee communication safety and session fidelity, but alone they cannot rule out deadlocks arising from the interleaving of different sessions. In Classical Processes (CP)$-$a process calculus based on classical linear logic$-
Externí odkaz:
http://arxiv.org/abs/2103.14466