Zobrazeno 1 - 10
of 12 053
pro vyhledávání: '"SCIMECA, A"'
Autor:
Scimeca, Manuel1,2,3,4 (AUTHOR) manuel.scimeca@uniroma2.it, Bonfiglio, Rita5 (AUTHOR) bonfiglio.rita@gmail.com, Menichini, Erika5 (AUTHOR) erika.menichini@gmail.com, Albonici, Loredana6 (AUTHOR) albonici@med.uniroma2.it, Urbano, Nicoletta7 (AUTHOR) n.urbano@virgilio.it, De Caro, Maria Teresa1 (AUTHOR) marydecaro12@gmail.com, Mauriello, Alessandro5 (AUTHOR) alessandro.mauriello@uniroma2.it, Schillaci, Orazio1,8 (AUTHOR) orazio.schillaci@uniroma2.it, Gambacurta, Alessandra5 (AUTHOR) gambacur@uniroma2.it, Bonanno, Elena5,9 (AUTHOR) elena.bonanno@uniroma2.it
Publikováno v:
International Journal of Molecular Sciences. Aug2024, Vol. 25 Issue 15, p8016. 3p.
Publikováno v:
Repositório Institucional da UNESPUniversidade Estadual PaulistaUNESP.
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Approved for entry into archive b
Approved for entry into archive b
Externí odkaz:
http://hdl.handle.net/11449/150296
Training a diverse ensemble of models has several practical applications such as providing candidates for model selection with better out-of-distribution (OOD) generalization, and enabling the detection of OOD samples via Bayesian principles. An exis
Externí odkaz:
http://arxiv.org/abs/2409.16797
Autor:
Venkatraman, Siddarth, Jain, Moksh, Scimeca, Luca, Kim, Minsu, Sendera, Marcin, Hasan, Mohsin, Rowe, Luke, Mittal, Sarthak, Lemos, Pablo, Bengio, Emmanuel, Adam, Alexandre, Rector-Brooks, Jarrid, Bengio, Yoshua, Berseth, Glen, Malkin, Nikolay
Diffusion models have emerged as effective distribution estimators in vision, language, and reinforcement learning, but their use as priors in downstream tasks poses an intractable posterior inference problem. This paper studies amortized sampling of
Externí odkaz:
http://arxiv.org/abs/2405.20971
Autor:
Sendera, Marcin, Kim, Minsu, Mittal, Sarthak, Lemos, Pablo, Scimeca, Luca, Rector-Brooks, Jarrid, Adam, Alexandre, Bengio, Yoshua, Malkin, Nikolay
We study the problem of training diffusion models to sample from a distribution with a given unnormalized density or energy function. We benchmark several diffusion-structured inference methods, including simulation-based variational approaches and o
Externí odkaz:
http://arxiv.org/abs/2402.05098
Autor:
Scimeca, Luca, Rubinstein, Alexander, Teney, Damien, Oh, Seong Joon, Nicolicioiu, Armand Mihai, Bengio, Yoshua
Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to a phenomenon known as shortcut learning, where a model relies on erroneous, easy-to-learn cues while ignoring reliable ones. In this work, we pr
Externí odkaz:
http://arxiv.org/abs/2311.16176
Autor:
Scimeca, Luca, Rubinstein, Alexander, Nicolicioiu, Armand Mihai, Teney, Damien, Bengio, Yoshua
Spurious correlations in the data, where multiple cues are predictive of the target labels, often lead to shortcut learning phenomena, where a model may rely on erroneous, easy-to-learn, cues while ignoring reliable ones. In this work, we propose an
Externí odkaz:
http://arxiv.org/abs/2310.02230
Publikováno v:
Current Oncology, Vol 31, Iss 10, Pp 6395-6405 (2024)
Introduction: Congruent with most guideline publishers, the Canadian Urological Association (CUA) recommends shared decision-making (SDM) on PSA screening (PSAS) for prostate cancer (PCa) following a discussion of its benefits and harms. However, the
Externí odkaz:
https://doaj.org/article/8a47c58e8bd1473f857a144ac344d525
Autor:
Scimeca, Giuseppe1 (AUTHOR) giuseppescimeca@me.com
Publikováno v:
Clinical Case Studies. Dec2024, Vol. 23 Issue 6, p457-478. 22p.
Autor:
Sung, Jee Eun1 (AUTHOR), Scimeca, Michael2 (AUTHOR), Li, Ran3 (AUTHOR), Kiran, Swathi2 (AUTHOR)
Publikováno v:
American Journal of Speech-Language Pathology. Nov2024, Vol. 33 Issue 6, p2717-2731. 15p.