Efficient predictive uncertainty estimators for deep probabilistic models (2020)
- Authors:
- USP affiliated authors: MAUÁ, DENIS DERATANI - IME ; LLERENA, JULISSA GIULIANA VILLANUEVA - IME
- Unidade: IME
- DOI: 10.1609/aaai.v34i10.7142
- Assunto: MODELOS PARA PROCESSOS ESTOCÁSTICOS
- Agências de fomento:
- Language: Inglês
- Imprenta:
- Publisher: AAAI Press
- Publisher place: Palo Alto
- Date published: 2020
- Source:
- Título do periódico: Proceddings : AAAI-20 Student Tracks
- ISSN: 2159-5399
- Volume/Número/Paginação/Ano: v. 35, n. 100, p. 13740-13741, 2020
- Conference titles: AAAI Conference on Artificial Intelligence - AAAI
- Este periódico é de acesso aberto
- Este artigo é de acesso aberto
- URL de acesso aberto
- Cor do Acesso Aberto: gold
-
ABNT
LLERENA, Julissa Villanueva e MAUÁ, Denis Deratani. Efficient predictive uncertainty estimators for deep probabilistic models. Proceddings : AAAI-20 Student Tracks. Palo Alto: AAAI Press. Disponível em: https://doi.org/10.1609/aaai.v34i10.7142. Acesso em: 23 abr. 2024. , 2020 -
APA
Llerena, J. V., & Mauá, D. D. (2020). Efficient predictive uncertainty estimators for deep probabilistic models. Proceddings : AAAI-20 Student Tracks. Palo Alto: AAAI Press. doi:10.1609/aaai.v34i10.7142 -
NLM
Llerena JV, Mauá DD. Efficient predictive uncertainty estimators for deep probabilistic models [Internet]. Proceddings : AAAI-20 Student Tracks. 2020 ; 35( 100): 13740-13741.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1609/aaai.v34i10.7142 -
Vancouver
Llerena JV, Mauá DD. Efficient predictive uncertainty estimators for deep probabilistic models [Internet]. Proceddings : AAAI-20 Student Tracks. 2020 ; 35( 100): 13740-13741.[citado 2024 abr. 23 ] Available from: https://doi.org/10.1609/aaai.v34i10.7142 - Cautious classification with data missing not at random using generative random forests
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Informações sobre o DOI: 10.1609/aaai.v34i10.7142 (Fonte: oaDOI API)
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