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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
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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: 26 maio 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 maio 26 ] 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 maio 26 ] 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
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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: 26 maio 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 maio 26 ] 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 maio 26 ] Available from: https://doi.org/10.1609/aaai.v34i10.7142
Artigo de periodico
The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference (2020)
Cozman, Fabio Gagliardi ; Mauá, Denis Deratani
Source: International Journal of Approximate Reasoning. Unidades: EP, IME
Subjects: PROGRAMAÇÃO LÓGICA, COMPUTABILIDADE E COMPLEXIDADE
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ABNT
COZMAN, Fabio Gagliardi e MAUÁ, Denis Deratani. The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference. International Journal of Approximate Reasoning, v. 125, p. 218-239, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2020.07.004. Acesso em: 26 maio 2024.
APA
Cozman, F. G., & Mauá, D. D. (2020). The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference. International Journal of Approximate Reasoning, 125, 218-239. doi:10.1016/j.ijar.2020.07.004
NLM
Cozman FG, Mauá DD. The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference [Internet]. International Journal of Approximate Reasoning. 2020 ; 125 218-239.[citado 2024 maio 26 ] Available from: https://doi.org/10.1016/j.ijar.2020.07.004
Vancouver
Cozman FG, Mauá DD. The joy of probabilistic answer set programming: semantics, complexity, expressivity, inference [Internet]. International Journal of Approximate Reasoning. 2020 ; 125 218-239.[citado 2024 maio 26 ] Available from: https://doi.org/10.1016/j.ijar.2020.07.004
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
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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: 26 maio 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 maio 26 ] 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 maio 26 ] 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
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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: 26 maio 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 maio 26 ] 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 maio 26 ] Available from: https://proceedings.mlr.press/v138/maua20a/maua20a.pdf
Artigo de periodico
Complexity results for probabilistic answer set programming (2020)
Mauá, Denis Deratani ; Cozman, Fabio Gagliardi
Source: International Journal of Approximate Reasoning. Unidades: IME, EP
Assunto: COMPUTABILIDADE E COMPLEXIDADE
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ABNT
MAUÁ, Denis Deratani e COZMAN, Fabio Gagliardi. Complexity results for probabilistic answer set programming. International Journal of Approximate Reasoning, v. 118, p. 133-154, 2020Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2019.12.003. Acesso em: 26 maio 2024.
APA
Mauá, D. D., & Cozman, F. G. (2020). Complexity results for probabilistic answer set programming. International Journal of Approximate Reasoning, 118, 133-154. doi:10.1016/j.ijar.2019.12.003
NLM
Mauá DD, Cozman FG. Complexity results for probabilistic answer set programming [Internet]. International Journal of Approximate Reasoning. 2020 ;118 133-154.[citado 2024 maio 26 ] Available from: https://doi.org/10.1016/j.ijar.2019.12.003
Vancouver
Mauá DD, Cozman FG. Complexity results for probabilistic answer set programming [Internet]. International Journal of Approximate Reasoning. 2020 ;118 133-154.[citado 2024 maio 26 ] Available from: https://doi.org/10.1016/j.ijar.2019.12.003
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
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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: 26 maio 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 maio 26 ] 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 maio 26 ] 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
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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: 26 maio 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 maio 26 ] 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 maio 26 ] 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
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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: 26 maio 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 maio 26 ] 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 maio 26 ] Available from: https://doi.org/10.1016/j.ijar.2020.08.009
Trabalho de evento
On the performance of planning through backpropagation (2020)
Scaroni, Renato; Bueno, Thiago Pereira; Barros, Leliane Nunes de ; Mauá, Denis Deratani
Source: Proceedings. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: IME
Subjects: APRENDIZAGEM PROFUNDA, COMPUTAÇÃO APLICADA, METODOLOGIA E TÉCNICAS DE COMPUTAÇÃO
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ABNT
SCARONI, Renato et al. On the performance of planning through backpropagation. 2020, Anais.. Cham: Springer, 2020. Disponível em: https://doi.org/10.1007/978-3-030-61380-8_8. Acesso em: 26 maio 2024.
APA
Scaroni, R., Bueno, T. P., Barros, L. N. de, & Mauá, D. D. (2020). On the performance of planning through backpropagation. In Proceedings. Cham: Springer. doi:10.1007/978-3-030-61380-8_8
NLM
Scaroni R, Bueno TP, Barros LN de, Mauá DD. On the performance of planning through backpropagation [Internet]. Proceedings. 2020 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_8
Vancouver
Scaroni R, Bueno TP, Barros LN de, Mauá DD. On the performance of planning through backpropagation [Internet]. Proceedings. 2020 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.1007/978-3-030-61380-8_8
Trabalho de evento
Learning probabilistic sentential decision diagrams by sampling (2020)
Geh, Renato Lui ; Mauá, Denis Deratani ; Antonucci, Alessandro
Source: Proceedings. Conference titles: Symposium on Knowledge Discovery, Mining and Learning - KDMiLe. Unidade: IME
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZADO COMPUTACIONAL, RACIOCÍNIO PROBABILÍSTICO
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ABNT
GEH, Renato Lui e MAUÁ, Denis Deratani e ANTONUCCI, Alessandro. Learning probabilistic sentential decision diagrams by sampling. 2020, Anais.. Porto Alegre: SBC, 2020. Disponível em: https://doi.org/10.5753/kdmile.2020.11968. Acesso em: 26 maio 2024.
APA
Geh, R. L., Mauá, D. D., & Antonucci, A. (2020). Learning probabilistic sentential decision diagrams by sampling. In Proceedings. Porto Alegre: SBC. doi:10.5753/kdmile.2020.11968
NLM
Geh RL, Mauá DD, Antonucci A. Learning probabilistic sentential decision diagrams by sampling [Internet]. Proceedings. 2020 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.5753/kdmile.2020.11968
Vancouver
Geh RL, Mauá DD, Antonucci A. Learning probabilistic sentential decision diagrams by sampling [Internet]. Proceedings. 2020 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.5753/kdmile.2020.11968
Trabalho de evento
Decision-aware model learning for actor-critic methods: when theory does not meet practice (2020)
Lovatto, Ângelo Gregório ; Bueno, Thiago Pereira ; Mauá, Denis Deratani ; Barros, Leliane Nunes de
Source: Proceedings. Conference titles: Conference on Neural Information Processing Systems - NeurIPS. Unidade: IME
Subjects: APRENDIZADO COMPUTACIONAL, MODELOS DE APRENDIZAGEM
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ABNT
LOVATTO, Ângelo Gregório et al. Decision-aware model learning for actor-critic methods: when theory does not meet practice. 2020, Anais.. San Diego: NeurIPS, 2020. Disponível em: https://openreview.net/pdf?id=a9lwn6v40C4. Acesso em: 26 maio 2024.
APA
Lovatto, Â. G., Bueno, T. P., Mauá, D. D., & Barros, L. N. de. (2020). Decision-aware model learning for actor-critic methods: when theory does not meet practice. In Proceedings. San Diego: NeurIPS. Recuperado de https://openreview.net/pdf?id=a9lwn6v40C4
NLM
Lovatto ÂG, Bueno TP, Mauá DD, Barros LN de. Decision-aware model learning for actor-critic methods: when theory does not meet practice [Internet]. Proceedings. 2020 ;[citado 2024 maio 26 ] Available from: https://openreview.net/pdf?id=a9lwn6v40C4
Vancouver
Lovatto ÂG, Bueno TP, Mauá DD, Barros LN de. Decision-aware model learning for actor-critic methods: when theory does not meet practice [Internet]. Proceedings. 2020 ;[citado 2024 maio 26 ] Available from: https://openreview.net/pdf?id=a9lwn6v40C4
Trabalho de evento
A contact network-based approach for online planning of containment measures for COVID-19 (2020)
Thomaz, Guilherme Yambanis ; Mauá, Denis Deratani ; Barros, Leliane Nunes de
Source: Anais. Conference titles: Encontro Nacional de Inteligência Artificial e Computacional - ENIAC. Unidade: IME
Subjects: PROCESSOS ESTOCÁSTICOS, REDES COMPLEXAS, COVID-19
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ABNT
THOMAZ, Guilherme Yambanis e MAUÁ, Denis Deratani e BARROS, Leliane Nunes de. A contact network-based approach for online planning of containment measures for COVID-19. 2020, Anais.. Porto Alegre: SBC, 2020. Disponível em: https://doi.org/10.5753/eniac.2020.12132. Acesso em: 26 maio 2024.
APA
Thomaz, G. Y., Mauá, D. D., & Barros, L. N. de. (2020). A contact network-based approach for online planning of containment measures for COVID-19. In Anais. Porto Alegre: SBC. doi:10.5753/eniac.2020.12132
NLM
Thomaz GY, Mauá DD, Barros LN de. A contact network-based approach for online planning of containment measures for COVID-19 [Internet]. Anais. 2020 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.5753/eniac.2020.12132
Vancouver
Thomaz GY, Mauá DD, Barros LN de. A contact network-based approach for online planning of containment measures for COVID-19 [Internet]. Anais. 2020 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.5753/eniac.2020.12132
Trabalho de evento
Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks (2020)
Deberaldini Netto, Caio Fabrício; Tannuri, Eduardo Aoun ; Mauá, Denis Deratani ; Cozman, Fabio Gagliardi
Source: Proceedings. Conference titles: Symposium on Knowledge Discovery, Mining and Learning - KDMiLe. Unidades: EP, IME
Subjects: REDES NEURAIS, ANÁLISE DE SÉRIES TEMPORAIS
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ABNT
DEBERALDINI NETTO, Caio Fabrício et al. Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks. 2020, Anais.. Porto Alegre: SBC, 2020. Disponível em: https://doi.org/10.5753/kdmile.2020.11981. Acesso em: 26 maio 2024.
APA
Deberaldini Netto, C. F., Tannuri, E. A., Mauá, D. D., & Cozman, F. G. (2020). Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks. In Proceedings. Porto Alegre: SBC. doi:10.5753/kdmile.2020.11981
NLM
Deberaldini Netto CF, Tannuri EA, Mauá DD, Cozman FG. Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks [Internet]. Proceedings. 2020 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.5753/kdmile.2020.11981
Vancouver
Deberaldini Netto CF, Tannuri EA, Mauá DD, Cozman FG. Prediction of environmental conditions for maritime navigation using a network of sensors: a practical application of graph neural networks [Internet]. Proceedings. 2020 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.5753/kdmile.2020.11981
Trabalho de evento
Deep reactive policies for planning in stochastic nonlinear domains (2019)
Bueno, Thiago Pereira; Barros, Leliane Nunes de ; Mauá, Denis Deratani ; Sanner, Scott
Source: Proceedings. Conference titles: AAAI Conference on Artificial Intelligence. Unidade: IME
Assunto: APRENDIZADO COMPUTACIONAL
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ABNT
BUENO, Thiago Pereira et al. Deep reactive policies for planning in stochastic nonlinear domains. 2019, Anais.. Palo Alto: AAAI, 2019. Disponível em: https://doi.org/10.1609/aaai.v33i01.33017530. Acesso em: 26 maio 2024.
APA
Bueno, T. P., Barros, L. N. de, Mauá, D. D., & Sanner, S. (2019). Deep reactive policies for planning in stochastic nonlinear domains. In Proceedings. Palo Alto: AAAI. doi:10.1609/aaai.v33i01.33017530
NLM
Bueno TP, Barros LN de, Mauá DD, Sanner S. Deep reactive policies for planning in stochastic nonlinear domains [Internet]. Proceedings. 2019 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.1609/aaai.v33i01.33017530
Vancouver
Bueno TP, Barros LN de, Mauá DD, Sanner S. Deep reactive policies for planning in stochastic nonlinear domains [Internet]. Proceedings. 2019 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.1609/aaai.v33i01.33017530
Trabalho de evento-anais periodico
Robust analysis of MAP inference in selective sum-product nNetworks (2019)
Villanueva Llerena, Julissa Giuliana ; Mauá, Denis Deratani
Source: Proceedings of Machine Learning Research : PMLR. Conference titles: International Symposium on Imprecise Probabilities: Theories and Applications - ISIPTA. Unidade: IME
Subjects: INTELIGÊNCIA ARTIFICIAL, RACIOCÍNIO PROBABILÍSTICO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VILLANUEVA LLERENA, Julissa Giuliana e MAUÁ, Denis Deratani. Robust analysis of MAP inference in selective sum-product nNetworks. Proceedings of Machine Learning Research : PMLR. Brookline: Microtome Publishing. Disponível em: http://proceedings.mlr.press/v103/villanueva-llerena19a/villanueva-llerena19a.pdf. Acesso em: 26 maio 2024. , 2019
APA
Villanueva Llerena, J. G., & Mauá, D. D. (2019). Robust analysis of MAP inference in selective sum-product nNetworks. Proceedings of Machine Learning Research : PMLR. Brookline: Microtome Publishing. Recuperado de http://proceedings.mlr.press/v103/villanueva-llerena19a/villanueva-llerena19a.pdf
NLM
Villanueva Llerena JG, Mauá DD. Robust analysis of MAP inference in selective sum-product nNetworks [Internet]. Proceedings of Machine Learning Research : PMLR. 2019 ; 103 430–440.[citado 2024 maio 26 ] Available from: http://proceedings.mlr.press/v103/villanueva-llerena19a/villanueva-llerena19a.pdf
Vancouver
Villanueva Llerena JG, Mauá DD. Robust analysis of MAP inference in selective sum-product nNetworks [Internet]. Proceedings of Machine Learning Research : PMLR. 2019 ; 103 430–440.[citado 2024 maio 26 ] Available from: http://proceedings.mlr.press/v103/villanueva-llerena19a/villanueva-llerena19a.pdf
Trabalho de evento
End-to-end imitation learning of lane following policies using sum-product networks (2019)
Geh, Renato Lui; Mauá, Denis Deratani
Source: Anais. Conference titles: Brazilian Conference on Intelligent Systems - BRACIS. Unidade: IME
Subjects: INTELIGÊNCIA ARTIFICIAL, APRENDIZAGEM PROFUNDA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GEH, Renato Lui e MAUÁ, Denis Deratani. End-to-end imitation learning of lane following policies using sum-product networks. 2019, Anais.. Porto Alegre: SBC, 2019. Disponível em: https://doi.org/10.5753/eniac.2019.9292. Acesso em: 26 maio 2024.
APA
Geh, R. L., & Mauá, D. D. (2019). End-to-end imitation learning of lane following policies using sum-product networks. In Anais. Porto Alegre: SBC. doi:10.5753/eniac.2019.9292
NLM
Geh RL, Mauá DD. End-to-end imitation learning of lane following policies using sum-product networks [Internet]. Anais. 2019 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.5753/eniac.2019.9292
Vancouver
Geh RL, Mauá DD. End-to-end imitation learning of lane following policies using sum-product networks [Internet]. Anais. 2019 ;[citado 2024 maio 26 ] Available from: https://doi.org/10.5753/eniac.2019.9292
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: 26 maio 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 maio 26 ] 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 maio 26 ] Available from: https://drive.google.com/file/d/1PkckPpeLUOP_Oeik_q4MprD6ZJeekqHZ/view
Artigo de periodico
The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws (2019)
Cozman, Fabio Gagliardi ; Mauá, Denis Deratani
Source: International Journal of Approximate Reasoning. Unidades: EP, IME
Subjects: TEORIA DA COMPUTAÇÃO, TEORIA DOS MODELOS, AQUISIÇÃO DE CONHECIMENTO, LÓGICA MATEMÁTICA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
COZMAN, Fabio Gagliardi e MAUÁ, Denis Deratani. The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws. International Journal of Approximate Reasoning, v. 110, p. 107-126, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2019.04.003. Acesso em: 26 maio 2024.
APA
Cozman, F. G., & Mauá, D. D. (2019). The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws. International Journal of Approximate Reasoning, 110, 107-126. doi:10.1016/j.ijar.2019.04.003
NLM
Cozman FG, Mauá DD. The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws [Internet]. International Journal of Approximate Reasoning. 2019 ; 110 107-126.[citado 2024 maio 26 ] Available from: https://doi.org/10.1016/j.ijar.2019.04.003
Vancouver
Cozman FG, Mauá DD. The finite model theory of Bayesian network specifications: Descriptive complexity and zero/one laws [Internet]. International Journal of Approximate Reasoning. 2019 ; 110 107-126.[citado 2024 maio 26 ] Available from: https://doi.org/10.1016/j.ijar.2019.04.003
Artigo de periodico
Speeding up parameter and rule learning for acyclic probabilistic logic programs (2019)
Faria, Francisco Henrique Otte Vieira de ; Gusmão, Arthur Colombini ; De Bona, Glauber ; Mauá, Denis Deratani ; Cozman, Fabio Gagliardi
Source: International Journal of Approximate Reasoning. Unidades: EP, IME
Subjects: PROGRAMAÇÃO LÓGICA, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
FARIA, Francisco Henrique Otte Vieira de et al. Speeding up parameter and rule learning for acyclic probabilistic logic programs. International Journal of Approximate Reasoning, v. 106, p. 32-50, 2019Tradução . . Disponível em: https://doi.org/10.1016/j.ijar.2018.12.012. Acesso em: 26 maio 2024.
APA
Faria, F. H. O. V. de, Gusmão, A. C., De Bona, G., Mauá, D. D., & Cozman, F. G. (2019). Speeding up parameter and rule learning for acyclic probabilistic logic programs. International Journal of Approximate Reasoning, 106, 32-50. doi:10.1016/j.ijar.2018.12.012
NLM
Faria FHOV de, Gusmão AC, De Bona G, Mauá DD, Cozman FG. Speeding up parameter and rule learning for acyclic probabilistic logic programs [Internet]. International Journal of Approximate Reasoning. 2019 ; 106 32-50.[citado 2024 maio 26 ] Available from: https://doi.org/10.1016/j.ijar.2018.12.012
Vancouver
Faria FHOV de, Gusmão AC, De Bona G, Mauá DD, Cozman FG. Speeding up parameter and rule learning for acyclic probabilistic logic programs [Internet]. International Journal of Approximate Reasoning. 2019 ; 106 32-50.[citado 2024 maio 26 ] Available from: https://doi.org/10.1016/j.ijar.2018.12.012

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