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pro vyhledávání: '"Constraints and Machine Learning"'
The generation of feasible adversarial examples is necessary for properly assessing models that work in constrained feature space. However, it remains a challenging task to enforce constraints into attacks that were designed for computer vision. We p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff435768829c1626c646706f3d50def7
http://arxiv.org/abs/2112.01156
http://arxiv.org/abs/2112.01156
Publikováno v:
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI 2020)
Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI 2020), Jan 2021, Yokohama ( virtual ), Japan. pp.1265-1271, ⟨10.24963/ijcai.2020/176⟩
IJCAI
Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI 2020)
Twenty-Ninth International Joint Conference on Artificial Intelligence (IJCAI 2020), Jan 2021, Yokohama ( virtual ), Japan. pp.1265-1271, ⟨10.24963/ijcai.2020/176⟩
IJCAI
While traditional data mining techniques have been used extensively for finding patterns in databases, they are not always suitable for incorporating user-specified constraints. To overcome this issue, CP and SAT based frameworks for modeling and sol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a1a197afcf27ca2ff716a22322ebb5ee
https://hal.archives-ouvertes.fr/hal-03092028
https://hal.archives-ouvertes.fr/hal-03092028
Publikováno v:
IJCAI
Rader, A P, Mocanu, I G, Belle, V & Juba, B 2021, Learning Implicitly with Noisy Data in Linear Arithmetic . in Proceedings of 30th International Joint Conference on Artificial Intelligence (IJCAI-21) . pp. 1410-1417, 30th International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada, 19/08/21 . https://doi.org/10.24963/ijcai.2021/195
Rader, A P, Mocanu, I G, Belle, V & Juba, B 2021, Learning Implicitly with Noisy Data in Linear Arithmetic . in Proceedings of 30th International Joint Conference on Artificial Intelligence (IJCAI-21) . pp. 1410-1417, 30th International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada, 19/08/21 . https://doi.org/10.24963/ijcai.2021/195
Robust learning in expressive languages with real-world data continues to be a challenging task. Numerous conventional methods appeal to heuristics without any assurances of robustness. While probably approximately correct (PAC) Semantics offers stro
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::115e2814e841e5d05156bc740d4bb3b4