ReP USP - Resultado da busca

Trabalho de evento
On modal clustering with Gaussian Sum-Product Networks (2023)
Madeira, Tiago; Mauá, Denis Deratani
Source: Proceedings. Conference titles: Conference on Uncertainty in Artificial Intelligence. Unidade: IME
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, APRENDIZADO COMPUTACIONAL, RACIOCÍNIO PROBABILÍSTICO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MADEIRA, Tiago e MAUÁ, Denis Deratani. On modal clustering with Gaussian Sum-Product Networks. 2023, Anais.. Corvallis: AUAI Press, 2023. Disponível em: https://openreview.net/pdf?id=dtzAUOmHqa. Acesso em: 07 nov. 2024.
APA
Madeira, T., & Mauá, D. D. (2023). On modal clustering with Gaussian Sum-Product Networks. In Proceedings. Corvallis: AUAI Press. Recuperado de https://openreview.net/pdf?id=dtzAUOmHqa
NLM
Madeira T, Mauá DD. On modal clustering with Gaussian Sum-Product Networks [Internet]. Proceedings. 2023 ;[citado 2024 nov. 07 ] Available from: https://openreview.net/pdf?id=dtzAUOmHqa
Vancouver
Madeira T, Mauá DD. On modal clustering with Gaussian Sum-Product Networks [Internet]. Proceedings. 2023 ;[citado 2024 nov. 07 ] Available from: https://openreview.net/pdf?id=dtzAUOmHqa
Trabalho de evento
Scalable learning of probabilistic circuits (2023)
Geh, Renato Lui ; Mauá, Denis Deratani
Source: Anais. Conference titles: Congresso da Sociedade Brasileira de Computação - CSBC. Unidade: IME
Subjects: APRENDIZADO COMPUTACIONAL, MODELOS PARA PROCESSOS ESTOCÁSTICOS, INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GEH, Renato Lui e MAUÁ, Denis Deratani. Scalable learning of probabilistic circuits. 2023, Anais.. Porto Alegre: SBC, 2023. Disponível em: https://doi.org/10.5753/ctd.2023.229457. Acesso em: 07 nov. 2024.
APA
Geh, R. L., & Mauá, D. D. (2023). Scalable learning of probabilistic circuits. In Anais. Porto Alegre: SBC. doi:10.5753/ctd.2023.229457
NLM
Geh RL, Mauá DD. Scalable learning of probabilistic circuits [Internet]. Anais. 2023 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.5753/ctd.2023.229457
Vancouver
Geh RL, Mauá DD. Scalable learning of probabilistic circuits [Internet]. Anais. 2023 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.5753/ctd.2023.229457
Artigo de periodico
A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification (2023)
Relvas, Carlos E. M.; Nakata, Asuka; Chen, Guoan; Beer, David G. ; Gotoh, Noriko ; Fujita, André
Source: Journal of Bioinformatics and Computational Biology. Unidade: IME
Subjects: BIOINFORMÁTICA, NEOPLASIAS PULMONARES, MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RELVAS, Carlos E. M. et al. A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification. Journal of Bioinformatics and Computational Biology, v. 21, n. artigo 2350019, p. 1-26, 2023Tradução . . Disponível em: https://doi.org/10.1142/S0219720023500191. Acesso em: 07 nov. 2024.
APA
Relvas, C. E. M., Nakata, A., Chen, G., Beer, D. G., Gotoh, N., & Fujita, A. (2023). A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification. Journal of Bioinformatics and Computational Biology, 21( artigo 2350019), 1-26. doi:10.1142/S0219720023500191
NLM
Relvas CEM, Nakata A, Chen G, Beer DG, Gotoh N, Fujita A. A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification [Internet]. Journal of Bioinformatics and Computational Biology. 2023 ; 21( artigo 2350019): 1-26.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1142/S0219720023500191
Vancouver
Relvas CEM, Nakata A, Chen G, Beer DG, Gotoh N, Fujita A. A model-based clustering algorithm with covariates adjustment and its application to lung cancer stratification [Internet]. Journal of Bioinformatics and Computational Biology. 2023 ; 21( artigo 2350019): 1-26.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1142/S0219720023500191
Trabalho de evento
Symbolic FOND planning for temporally extended goals (2022)
Santos, Viviane Bonadia dos ; Barros, Leliane Nunes de ; Pereira, Silvio do Lago; Menezes, Maria Viviane de
Source: Workshop. Conference titles: International Conference on Automated Planning and Scheduling - ICAPS. Unidade: IME
Subjects: INTELIGÊNCIA ARTIFICIAL, MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
SANTOS, Viviane Bonadia dos et al. Symbolic FOND planning for temporally extended goals. 2022, Anais.. Singapore: Instituto de Matemática e Estatística, Universidade de São Paulo, 2022. Disponível em: http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_4759.pdf. Acesso em: 07 nov. 2024.
APA
Santos, V. B. dos, Barros, L. N. de, Pereira, S. do L., & Menezes, M. V. de. (2022). Symbolic FOND planning for temporally extended goals. In Workshop. Singapore: Instituto de Matemática e Estatística, Universidade de São Paulo. Recuperado de http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_4759.pdf
NLM
Santos VB dos, Barros LN de, Pereira S do L, Menezes MV de. Symbolic FOND planning for temporally extended goals [Internet]. Workshop. 2022 ;[citado 2024 nov. 07 ] Available from: http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_4759.pdf
Vancouver
Santos VB dos, Barros LN de, Pereira S do L, Menezes MV de. Symbolic FOND planning for temporally extended goals [Internet]. Workshop. 2022 ;[citado 2024 nov. 07 ] Available from: http://icaps22.icaps-conference.org/workshops/KEPS/KEPS-22_paper_4759.pdf
Trabalho de evento
Tractable classification with non-ignorable missing data using generative random forests (2022)
Villanueva Llerena, Julissa Giuliana ; Mauá, Denis Deratani
Source: Anais. Conference titles: Symposium on Knowledge Discovery, Mining and Learning - KDMiLe. Unidade: IME
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VILLANUEVA LLERENA, Julissa Giuliana e MAUÁ, Denis Deratani. Tractable classification with non-ignorable missing data using generative random forests. 2022, Anais.. Porto Alegre: SBC, 2022. Disponível em: https://doi.org/10.5753/kdmile.2022.227969. Acesso em: 07 nov. 2024.
APA
Villanueva Llerena, J. G., & Mauá, D. D. (2022). Tractable classification with non-ignorable missing data using generative random forests. In Anais. Porto Alegre: SBC. doi:10.5753/kdmile.2022.227969
NLM
Villanueva Llerena JG, Mauá DD. Tractable classification with non-ignorable missing data using generative random forests [Internet]. Anais. 2022 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.5753/kdmile.2022.227969
Vancouver
Villanueva Llerena JG, Mauá DD. Tractable classification with non-ignorable missing data using generative random forests [Internet]. Anais. 2022 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.5753/kdmile.2022.227969
Trabalho de evento
Differentiable planning for optimal liquidation (2022)
Pennacchio, Alan Assis; Barros, Leliane Nunes de ; Mauá, Denis Deratani
Source: Anais. Conference titles: Brazilian Workshop on Artificial Intelligence in Finance - BWAIF. Unidade: IME
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, INTELIGÊNCIA ARTIFICIAL, FINANÇAS, COMPUTAÇÃO APLICADA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
PENNACCHIO, Alan Assis e BARROS, Leliane Nunes de e MAUÁ, Denis Deratani. Differentiable planning for optimal liquidation. 2022, Anais.. Porto Alegre: SBC, 2022. Disponível em: https://doi.org/10.5753/bwaif.2022.223144. Acesso em: 07 nov. 2024.
APA
Pennacchio, A. A., Barros, L. N. de, & Mauá, D. D. (2022). Differentiable planning for optimal liquidation. In Anais. Porto Alegre: SBC. doi:10.5753/bwaif.2022.223144
NLM
Pennacchio AA, Barros LN de, Mauá DD. Differentiable planning for optimal liquidation [Internet]. Anais. 2022 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.5753/bwaif.2022.223144
Vancouver
Pennacchio AA, Barros LN de, Mauá DD. Differentiable planning for optimal liquidation [Internet]. Anais. 2022 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.5753/bwaif.2022.223144
Trabalho de evento
Tractable mode-finding in sum-product networks with Gaussian leaves (2022)
Madeira, Tiago; Mauá, Denis Deratani
Source: Proceedings. Conference titles: Encontro Nacional de Inteligência Artificial e Computacional. Unidade: IME
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, APRENDIZADO COMPUTACIONAL, RACIOCÍNIO PROBABILÍSTICO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MADEIRA, Tiago e MAUÁ, Denis Deratani. Tractable mode-finding in sum-product networks with Gaussian leaves. 2022, Anais.. Porto Alegre: SBC, 2022. Disponível em: https://doi.org/10.5753/eniac.2022.227582. Acesso em: 07 nov. 2024.
APA
Madeira, T., & Mauá, D. D. (2022). Tractable mode-finding in sum-product networks with Gaussian leaves. In Proceedings. Porto Alegre: SBC. doi:10.5753/eniac.2022.227582
NLM
Madeira T, Mauá DD. Tractable mode-finding in sum-product networks with Gaussian leaves [Internet]. Proceedings. 2022 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.5753/eniac.2022.227582
Vancouver
Madeira T, Mauá DD. Tractable mode-finding in sum-product networks with Gaussian leaves [Internet]. Proceedings. 2022 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.5753/eniac.2022.227582
Editor de periodico
International Journal of Approximate Reasoning (2021)
Mauá, Denis Deratani (Editor) ; Campos, Cassio Polpo de (Editor)
Unidade: IME
Assunto: MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
International Journal of Approximate Reasoning. . Philadelphia: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://www.sciencedirect.com/journal/international-journal-of-approximate-reasoning/vol/137/suppl/C. Acesso em: 07 nov. 2024. , 2021
APA
International Journal of Approximate Reasoning. (2021). International Journal of Approximate Reasoning. Philadelphia: Instituto de Matemática e Estatística, Universidade de São Paulo. Recuperado de https://www.sciencedirect.com/journal/international-journal-of-approximate-reasoning/vol/137/suppl/C
NLM
International Journal of Approximate Reasoning [Internet]. 2021 ; 137 113.[citado 2024 nov. 07 ] Available from: https://www.sciencedirect.com/journal/international-journal-of-approximate-reasoning/vol/137/suppl/C
Vancouver
International Journal of Approximate Reasoning [Internet]. 2021 ; 137 113.[citado 2024 nov. 07 ] Available from: https://www.sciencedirect.com/journal/international-journal-of-approximate-reasoning/vol/137/suppl/C
Trabalho de evento
Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments (2021)
Lovatto, Ângelo Gregório ; Bueno, Thiago Pereira ; Barros, Leliane Nunes de
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: IME
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
LOVATTO, Ângelo Gregório e BUENO, Thiago Pereira e BARROS, Leliane Nunes de. Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments. 2021, Anais.. Cham: Springer, 2021. Disponível em: https://doi.org/10.1007/978-3-030-91702-9_3. Acesso em: 07 nov. 2024.
APA
Lovatto, Â. G., Bueno, T. P., & Barros, L. N. de. (2021). Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments. In Proceedings. Cham: Springer. doi:10.1007/978-3-030-91702-9_3
NLM
Lovatto ÂG, Bueno TP, Barros LN de. Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments [Internet]. Proceedings. 2021 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1007/978-3-030-91702-9_3
Vancouver
Lovatto ÂG, Bueno TP, Barros LN de. Gradient estimation in model-based reinforcement learning: a study on linear quadratic environments [Internet]. Proceedings. 2021 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1007/978-3-030-91702-9_3
Trabalho de evento
Cautious classification with data missing not at random using generative random forests (2021)
Villanueva Llerena, Julissa Giuliana ; Mauá, Denis Deratani ; Antonucci, Alessandro
Source: Proceedings. Conference titles: European Conference on Symbolic and Quantitative Approaches with Uncertainty - ECSQARU. Unidade: IME
Subjects: APRENDIZADO COMPUTACIONAL, MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VILLANUEVA LLERENA, Julissa Giuliana e MAUÁ, Denis Deratani e ANTONUCCI, Alessandro. Cautious classification with data missing not at random using generative random forests. 2021, Anais.. Cham: Springer, 2021. Disponível em: https://doi.org/10.1007/978-3-030-86772-0_21. Acesso em: 07 nov. 2024.
APA
Villanueva Llerena, J. G., Mauá, D. D., & Antonucci, A. (2021). Cautious classification with data missing not at random using generative random forests. In Proceedings. Cham: Springer. doi:10.1007/978-3-030-86772-0_21
NLM
Villanueva Llerena JG, Mauá DD, Antonucci A. Cautious classification with data missing not at random using generative random forests [Internet]. Proceedings. 2021 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1007/978-3-030-86772-0_21
Vancouver
Villanueva Llerena JG, Mauá DD, Antonucci A. Cautious classification with data missing not at random using generative random forests [Internet]. Proceedings. 2021 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1007/978-3-030-86772-0_21
Trabalho de evento
Fast and accurate learning of probabilistic circuits by random projections (2021)
Geh, Renato Lui ; Mauá, Denis Deratani
Source: Workshop. Conference titles: Workshop on Tractable Probabilistic Modeling. Unidade: IME
Subjects: APRENDIZADO COMPUTACIONAL, MODELOS PARA PROCESSOS ESTOCÁSTICOS, INTELIGÊNCIA ARTIFICIAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GEH, Renato Lui e MAUÁ, Denis Deratani. Fast and accurate learning of probabilistic circuits by random projections. 2021, Anais.. [S.l.]: Instituto de Matemática e Estatística, Universidade de São Paulo, 2021. Disponível em: https://openreview.net/forum?id=BhoGeih_B8o. Acesso em: 07 nov. 2024.
APA
Geh, R. L., & Mauá, D. D. (2021). Fast and accurate learning of probabilistic circuits by random projections. In Workshop. Instituto de Matemática e Estatística, Universidade de São Paulo. Recuperado de https://openreview.net/forum?id=BhoGeih_B8o
NLM
Geh RL, Mauá DD. Fast and accurate learning of probabilistic circuits by random projections [Internet]. Workshop. 2021 ;[citado 2024 nov. 07 ] Available from: https://openreview.net/forum?id=BhoGeih_B8o
Vancouver
Geh RL, Mauá DD. Fast and accurate learning of probabilistic circuits by random projections [Internet]. Workshop. 2021 ;[citado 2024 nov. 07 ] Available from: https://openreview.net/forum?id=BhoGeih_B8o
Artigo de periodico-carta/editorial
Special issue on robustness in probabilistic graphical models. [Editorial] (2021)
Mauá, Denis Deratani ; Campos, Cassio Polpo de
Source: International Journal of Approximate Reasoning. Unidade: IME
Assunto: MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MAUÁ, Denis Deratani e CAMPOS, Cassio Polpo de. Special issue on robustness in probabilistic graphical models. [Editorial]. International Journal of Approximate Reasoning. Philadelphia: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://doi.org/10.1016/j.ijar.2021.07.002. Acesso em: 07 nov. 2024. , 2021
APA
Mauá, D. D., & Campos, C. P. de. (2021). Special issue on robustness in probabilistic graphical models. [Editorial]. International Journal of Approximate Reasoning. Philadelphia: Instituto de Matemática e Estatística, Universidade de São Paulo. doi:10.1016/j.ijar.2021.07.002
NLM
Mauá DD, Campos CP de. Special issue on robustness in probabilistic graphical models. [Editorial] [Internet]. International Journal of Approximate Reasoning. 2021 ; 137 113.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.ijar.2021.07.002
Vancouver
Mauá DD, Campos CP de. Special issue on robustness in probabilistic graphical models. [Editorial] [Internet]. International Journal of Approximate Reasoning. 2021 ; 137 113.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.ijar.2021.07.002
Trabalho de evento-resumo periodico
Efficient predictive uncertainty estimators for deep probabilistic models (2020)
Llerena, Julissa Villanueva ; Mauá, Denis Deratani
Source: Proceddings : AAAI-20 Student Tracks. Conference titles: AAAI Conference on Artificial Intelligence - AAAI. Unidade: IME
Assunto: MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
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: 07 nov. 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 nov. 07 ] 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 nov. 07 ] Available from: https://doi.org/10.1609/aaai.v34i10.7142
Trabalho de evento
Finding feasible policies for extreme risk-averse agents in probabilistic planning (2020)
Fernandez, Milton Condori; Barros, Leliane Nunes de ; Mauá, Denis Deratani ; Delgado, Karina Valdivia ; Silva, Valdinei Freire da
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidades: IME, EACH
Subjects: PROBABILIDADE, MODELOS PARA PROCESSOS ESTOCÁSTICOS, ANÁLISE DE RISCO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FERNANDEZ, Milton Condori et al. Finding feasible policies for extreme risk-averse agents in probabilistic planning. 2020, Anais.. Cham: Springer, 2020. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_7. Acesso em: 07 nov. 2024.
APA
Fernandez, M. C., Barros, L. N. de, Mauá, D. D., Delgado, K. V., & Silva, V. F. da. (2020). Finding feasible policies for extreme risk-averse agents in probabilistic planning. In Proceedings. Cham: Springer. doi:10.1007/978-3-030-61380-8_7
NLM
Fernandez MC, Barros LN de, Mauá DD, Delgado KV, Silva VF da. Finding feasible policies for extreme risk-averse agents in probabilistic planning [Internet]. Proceedings. 2020 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_7
Vancouver
Fernandez MC, Barros LN de, Mauá DD, Delgado KV, Silva VF da. Finding feasible policies for extreme risk-averse agents in probabilistic planning [Internet]. Proceedings. 2020 ;[citado 2024 nov. 07 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_7
Trabalho de evento-anais periodico
Two reformulation approaches to maximum-a-posteriori inference in sum-product networks (2020)
Mauá, Denis Deratani ; Ribeiro, Heitor Reis; Katague, Gustavo Perez; Antonucci, Alessandro
Source: Proceedings of Machine Learning Research. Conference titles: International Conference on Probabilistic Graphical Models. Unidade: IME
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, PROGRAMAÇÃO LINEAR
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MAUÁ, Denis Deratani et al. Two reformulation approaches to maximum-a-posteriori inference in sum-product networks. Proceedings of Machine Learning Research. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://proceedings.mlr.press/v138/maua20a/maua20a.pdf. Acesso em: 07 nov. 2024. , 2020
APA
Mauá, D. D., Ribeiro, H. R., Katague, G. P., & Antonucci, A. (2020). Two reformulation approaches to maximum-a-posteriori inference in sum-product networks. Proceedings of Machine Learning Research. Brookline: Instituto de Matemática e Estatística, Universidade de São Paulo. Recuperado de https://proceedings.mlr.press/v138/maua20a/maua20a.pdf
NLM
Mauá DD, Ribeiro HR, Katague GP, Antonucci A. Two reformulation approaches to maximum-a-posteriori inference in sum-product networks [Internet]. Proceedings of Machine Learning Research. 2020 ; 138 293-304.[citado 2024 nov. 07 ] Available from: https://proceedings.mlr.press/v138/maua20a/maua20a.pdf
Vancouver
Mauá DD, Ribeiro HR, Katague GP, Antonucci A. Two reformulation approaches to maximum-a-posteriori inference in sum-product networks [Internet]. Proceedings of Machine Learning Research. 2020 ; 138 293-304.[citado 2024 nov. 07 ] Available from: https://proceedings.mlr.press/v138/maua20a/maua20a.pdf
Artigo de periodico
Tractable inference in credal sentential decision diagrams (2020)
Mattei, Lilith; Antonucci, Alessandro; Mauá, Denis Deratani ; Facchini, Alessandro ; Villanueva Llerena, Julissa Giuliana
Source: International Journal of Approximate Reasoning. Unidade: IME
Assunto: MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MATTEI, Lilith et al. Tractable inference in credal sentential decision diagrams. International Journal of Approximate Reasoning, v. 125, p. 26-48, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2020.06.005. Acesso em: 07 nov. 2024.
APA
Mattei, L., Antonucci, A., Mauá, D. D., Facchini, A., & Villanueva Llerena, J. G. (2020). Tractable inference in credal sentential decision diagrams. International Journal of Approximate Reasoning, 125, 26-48. doi:10.1016/j.ijar.2020.06.005
NLM
Mattei L, Antonucci A, Mauá DD, Facchini A, Villanueva Llerena JG. Tractable inference in credal sentential decision diagrams [Internet]. International Journal of Approximate Reasoning. 2020 ; 125 26-48.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.ijar.2020.06.005
Vancouver
Mattei L, Antonucci A, Mauá DD, Facchini A, Villanueva Llerena JG. Tractable inference in credal sentential decision diagrams [Internet]. International Journal of Approximate Reasoning. 2020 ; 125 26-48.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.ijar.2020.06.005
Artigo de periodico
Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks (2020)
Villanueva Llerena, Julissa ; Mauá, Denis Deratani
Source: International Journal of Approximate Reasoning. Unidade: IME
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VILLANUEVA LLERENA, Julissa e MAUÁ, Denis Deratani. Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks. International Journal of Approximate Reasoning, v. 126, p. 158-180-, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2020.07.008. Acesso em: 07 nov. 2024.
APA
Villanueva Llerena, J., & Mauá, D. D. (2020). Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks. International Journal of Approximate Reasoning, 126, 158-180-. doi:10.1016/j.ijar.2020.07.008
NLM
Villanueva Llerena J, Mauá DD. Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks [Internet]. International Journal of Approximate Reasoning. 2020 ; 126 158-180-.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.ijar.2020.07.008
Vancouver
Villanueva Llerena J, Mauá DD. Efficient algorithms for robustness analysis of maximum a posteriori inference in selective sum-product networks [Internet]. International Journal of Approximate Reasoning. 2020 ; 126 158-180-.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.ijar.2020.07.008
Artigo de periodico
Thirty years of credal networks: specification, algorithms and complexity (2020)
Mauá, Denis Deratani ; Cozman, Fabio Gagliardi
Source: International Journal of Approximate Reasoning. Unidades: IME, EP
Subjects: MODELOS PARA PROCESSOS ESTOCÁSTICOS, PROBABILIDADE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MAUÁ, Denis Deratani e COZMAN, Fabio Gagliardi. Thirty years of credal networks: specification, algorithms and complexity. International Journal of Approximate Reasoning, v. 126, p. 133-157, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2020.08.009. Acesso em: 07 nov. 2024.
APA
Mauá, D. D., & Cozman, F. G. (2020). Thirty years of credal networks: specification, algorithms and complexity. International Journal of Approximate Reasoning, 126, 133-157. doi:10.1016/j.ijar.2020.08.009
NLM
Mauá DD, Cozman FG. Thirty years of credal networks: specification, algorithms and complexity [Internet]. International Journal of Approximate Reasoning. 2020 ; 126 133-157.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.ijar.2020.08.009
Vancouver
Mauá DD, Cozman FG. Thirty years of credal networks: specification, algorithms and complexity [Internet]. International Journal of Approximate Reasoning. 2020 ; 126 133-157.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.ijar.2020.08.009
Trabalho de evento
Exploring the space of probabilistic sentential decision diagrams (2019)
Mattei, Lilith; Soares, Decio Lauro ; Antonucci, Alessandro ; Mauá, Denis Deratani ; Facchini, Alessandro
Source: Proceedings of Machine Learning Research - PMLR. Conference titles: Workshop on Tractable Probabilistic Modeling - TPM. Unidade: IME
Assunto: MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MATTEI, Lilith et al. Exploring the space of probabilistic sentential decision diagrams. 2019, Anais.. San Diego: International Conference on Machine Learning, 2019. Disponível em: https://drive.google.com/file/d/1PkckPpeLUOP_Oeik_q4MprD6ZJeekqHZ/view. Acesso em: 07 nov. 2024.
APA
Mattei, L., Soares, D. L., Antonucci, A., Mauá, D. D., & Facchini, A. (2019). Exploring the space of probabilistic sentential decision diagrams. In Proceedings of Machine Learning Research - PMLR. San Diego: International Conference on Machine Learning. Recuperado de https://drive.google.com/file/d/1PkckPpeLUOP_Oeik_q4MprD6ZJeekqHZ/view
NLM
Mattei L, Soares DL, Antonucci A, Mauá DD, Facchini A. Exploring the space of probabilistic sentential decision diagrams [Internet]. Proceedings of Machine Learning Research - PMLR. 2019 ;[citado 2024 nov. 07 ] Available from: https://drive.google.com/file/d/1PkckPpeLUOP_Oeik_q4MprD6ZJeekqHZ/view
Vancouver
Mattei L, Soares DL, Antonucci A, Mauá DD, Facchini A. Exploring the space of probabilistic sentential decision diagrams [Internet]. Proceedings of Machine Learning Research - PMLR. 2019 ;[citado 2024 nov. 07 ] Available from: https://drive.google.com/file/d/1PkckPpeLUOP_Oeik_q4MprD6ZJeekqHZ/view
Artigo de periodico
Robustifying sum-product networks (2018)
Mauá, Denis Deratani ; Conaty, Diarmaid; Cozman, Fabio Gagliardi ; Poppenhaeger, Katja; Campos, Cassio Polpo de
Source: International Journal of Approximate Reasoning. Unidades: IME, EP
Subjects: INTELIGÊNCIA ARTIFICIAL, MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
MAUÁ, Denis Deratani et al. Robustifying sum-product networks. International Journal of Approximate Reasoning, v. 101, p. 163-180, 2018Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2018.07.003. Acesso em: 07 nov. 2024.
APA
Mauá, D. D., Conaty, D., Cozman, F. G., Poppenhaeger, K., & Campos, C. P. de. (2018). Robustifying sum-product networks. International Journal of Approximate Reasoning, 101, 163-180. doi:10.1016/j.ijar.2018.07.003
NLM
Mauá DD, Conaty D, Cozman FG, Poppenhaeger K, Campos CP de. Robustifying sum-product networks [Internet]. International Journal of Approximate Reasoning. 2018 ; 101 163-180.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.ijar.2018.07.003
Vancouver
Mauá DD, Conaty D, Cozman FG, Poppenhaeger K, Campos CP de. Robustifying sum-product networks [Internet]. International Journal of Approximate Reasoning. 2018 ; 101 163-180.[citado 2024 nov. 07 ] Available from: https://doi.org/10.1016/j.ijar.2018.07.003

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