Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Matt Fredrikson"'
Autor:
Helena Montenegro, Wilson Silva, Alex Gaudio, Matt Fredrikson, Asim Smailagic, Jaime S. Cardoso
Publikováno v:
IEEE Access, Vol 10, Pp 28333-28347 (2022)
Deep Learning achieves state-of-the-art results in many domains, yet its black-box nature limits its application to real-world contexts. An intuitive way to improve the interpretability of Deep Learning models is by explaining their decisions with si
Externí odkaz:
https://doaj.org/article/03f766a91653426dbe58c26f66b41630
Publikováno v:
Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6185caddc94cf139cd0172a67f22058b
https://doi.org/10.1145/3498361.3539774
https://doi.org/10.1145/3498361.3539774
Publikováno v:
Proceedings of the 20th Annual International Conference on Mobile Systems, Applications and Services.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::307b240dc73fddca2a249c0cde5060a5
https://doi.org/10.1145/3498361.3538664
https://doi.org/10.1145/3498361.3538664
Publikováno v:
Journal of Computer Security. 28:35-70
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6e0cecda31ae5f08847da6c4d6e06ad5
https://doi.org/10.3233/jcs-191362
https://doi.org/10.3233/jcs-191362
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031212215
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8f07f46ae321fce0e06978b848188672
https://doi.org/10.1007/978-3-031-21222-2_7
https://doi.org/10.1007/978-3-031-21222-2_7
Publikováno v:
KDD
This tutorial examines the synergistic relationship between explainability methods for machine learning and a significant problem related to model quality: robustness against adversarial perturbations. We begin with a broad overview of approaches to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95e430a484bf8fcec082220bba3859f8
https://doi.org/10.1145/3447548.3470806
https://doi.org/10.1145/3447548.3470806
Publikováno v:
CSF
The risk posed by high-profile data breaches has raised the stakes for adhering to data access policies for many organizations, but the complexity of both the policies themselves and the applications that must obey them raises significant challenges.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0f2b88d7c97b59ee4fbb75fb75c689c
https://doi.org/10.1109/csf51468.2021.00001
https://doi.org/10.1109/csf51468.2021.00001
Autor:
Emily Black, Matt Fredrikson
Publikováno v:
FAccT
We introduce leave-one-out unfairness, which characterizes how likely a model's prediction for an individual will change due to the inclusion or removal of a single other person in the model's training data. Leave-one-out unfairness appeals to the id
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b358704517d40b7277647c049844cb8
https://doi.org/10.1145/3442188.3445894
https://doi.org/10.1145/3442188.3445894
Publikováno v:
Repositorio Abierto de la UdL
Universitad de Lleida
Universitad de Lleida
Current state-of-the-art research for tackling the problem of malware detection and classification is centered on the design, implementation and deployment of systems powered by machine learning because of its ability to generalize to never-before-se
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::afc3bc46faa59539469462c6a484cc31
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030670665
VMCAI
VMCAI
We study the problem of using probabilistic network models to formally analyze their quantitative properties, such as the effect of different load-balancing strategies on the long-term traffic on a server farm. Compared to prior work, we explore a di
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::24855681fa112fa2957f1b7e90665ecd
https://doi.org/10.1007/978-3-030-67067-2_22
https://doi.org/10.1007/978-3-030-67067-2_22