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Artigo de periodico
SAnDReS 2.0: development of machine-learning models to explore the scoring function space (2024)
Azevedo, Walter Filgueira de ; Quiroga, Rodrigo ; Villarreal, Marcos Ariel; Silveira, Nelson Jose Freitas da ; Ferreira, Gabriela Bitencourt ; Silva, Amauri Duarte da ; Acosta, Martina Veit ; Oliveira, Patricia Rufino; Tutone, Marco; Biziukova, Nadezhda ; Poroikov, Vladimir ; Tarasova, Olga ; Baud, Stéphaine
Source: Journal of Computational Chemistry. Unidade: EACH
Assunto: APRENDIZADO COMPUTACIONAL
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AZEVEDO, Walter Filgueira de et al. SAnDReS 2.0: development of machine-learning models to explore the scoring function space. Journal of Computational Chemistry, p. 01-14, 2024Tradução . . Disponível em: http://dx.doi.org/10.1002/jcc.27449. Acesso em: 29 jun. 2024.
APA
Azevedo, W. F. de, Quiroga, R., Villarreal, M. A., Silveira, N. J. F. da, Ferreira, G. B., Silva, A. D. da, et al. (2024). SAnDReS 2.0: development of machine-learning models to explore the scoring function space. Journal of Computational Chemistry, 01-14. doi:10.1002/jcc.27449
NLM
Azevedo WF de, Quiroga R, Villarreal MA, Silveira NJF da, Ferreira GB, Silva AD da, Acosta MV, Oliveira PR, Tutone M, Biziukova N, Poroikov V, Tarasova O, Baud S. SAnDReS 2.0: development of machine-learning models to explore the scoring function space [Internet]. Journal of Computational Chemistry. 2024 ; 01-14.[citado 2024 jun. 29 ] Available from: http://dx.doi.org/10.1002/jcc.27449
Vancouver
Azevedo WF de, Quiroga R, Villarreal MA, Silveira NJF da, Ferreira GB, Silva AD da, Acosta MV, Oliveira PR, Tutone M, Biziukova N, Poroikov V, Tarasova O, Baud S. SAnDReS 2.0: development of machine-learning models to explore the scoring function space [Internet]. Journal of Computational Chemistry. 2024 ; 01-14.[citado 2024 jun. 29 ] Available from: http://dx.doi.org/10.1002/jcc.27449
Artigo de periodico
SWINN: efficient nearest neighbor search in sliding windows using graphs (2024)
Mastelini, Saulo Martiello ; Veloso, Bruno; Halford, Max; Carvalho, André Carlos Ponce de Leon Ferreira de ; Gama, João
Source: Information Fusion. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, ALGORITMOS PARA PROCESSAMENTO
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MASTELINI, Saulo Martiello et al. SWINN: efficient nearest neighbor search in sliding windows using graphs. Information Fusion, v. 101, n. Ja 2024, p. 1-17, 2024Tradução . . Disponível em: https://doi.org/10.1016/j.inffus.2023.101979. Acesso em: 29 jun. 2024.
APA
Mastelini, S. M., Veloso, B., Halford, M., Carvalho, A. C. P. de L. F. de, & Gama, J. (2024). SWINN: efficient nearest neighbor search in sliding windows using graphs. Information Fusion, 101( Ja 2024), 1-17. doi:10.1016/j.inffus.2023.101979
NLM
Mastelini SM, Veloso B, Halford M, Carvalho ACP de LF de, Gama J. SWINN: efficient nearest neighbor search in sliding windows using graphs [Internet]. Information Fusion. 2024 ; 101( Ja 2024): 1-17.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1016/j.inffus.2023.101979
Vancouver
Mastelini SM, Veloso B, Halford M, Carvalho ACP de LF de, Gama J. SWINN: efficient nearest neighbor search in sliding windows using graphs [Internet]. Information Fusion. 2024 ; 101( Ja 2024): 1-17.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1016/j.inffus.2023.101979
Trabalho de evento-anais periodico
On limits of machine learning techniques in the learning ofscheduling policies (2023)
Rosa, Lucas de Sousa ; Carastan-Santos, Danilo ; Goldman, Alfredo ; Trystram, Denis
Source: Revista Eletrônica de Iniciação Científica em Computação. Conference titles: Concurso de Trabalhos de Iniciação Científica - CTIC. Unidade: IME
Subjects: PROGRAMAÇÃO PARALELA, HEURÍSTICA, APRENDIZADO COMPUTACIONAL
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ROSA, Lucas de Sousa et al. On limits of machine learning techniques in the learning ofscheduling policies. Revista Eletrônica de Iniciação Científica em Computação. Porto Alegre: Instituto de Matemática e Estatística, Universidade de São Paulo. Disponível em: https://sol.sbc.org.br/journals/index.php/reic/article/view/3419. Acesso em: 29 jun. 2024. , 2023
APA
Rosa, L. de S., Carastan-Santos, D., Goldman, A., & Trystram, D. (2023). On limits of machine learning techniques in the learning ofscheduling policies. Revista Eletrônica de Iniciação Científica em Computação. Porto Alegre: Instituto de Matemática e Estatística, Universidade de São Paulo. Recuperado de https://sol.sbc.org.br/journals/index.php/reic/article/view/3419
NLM
Rosa L de S, Carastan-Santos D, Goldman A, Trystram D. On limits of machine learning techniques in the learning ofscheduling policies [Internet]. Revista Eletrônica de Iniciação Científica em Computação. 2023 ; 21( 2): 61-70.[citado 2024 jun. 29 ] Available from: https://sol.sbc.org.br/journals/index.php/reic/article/view/3419
Vancouver
Rosa L de S, Carastan-Santos D, Goldman A, Trystram D. On limits of machine learning techniques in the learning ofscheduling policies [Internet]. Revista Eletrônica de Iniciação Científica em Computação. 2023 ; 21( 2): 61-70.[citado 2024 jun. 29 ] Available from: https://sol.sbc.org.br/journals/index.php/reic/article/view/3419
Artigo de periodico
State-of-the-art in the mechanistic modeling of the drying of solids: a review of 40 years of progress and perspectives (2023)
Perré, Patrick ; Rémond, Romain ; Almeida, Giana ; Augusto, Pedro Esteves Duarte ; Turner, Ian
Source: Drying Technology. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, MATEMÁTICA APLICADA, MODELAGEM DE DADOS, SECAGEM
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PERRÉ, Patrick et al. State-of-the-art in the mechanistic modeling of the drying of solids: a review of 40 years of progress and perspectives. Drying Technology, v. 41, n. 6, p. 817–842, 2023Tradução . . Disponível em: https://doi.org/10.1080/07373937.2022.2159974. Acesso em: 29 jun. 2024.
APA
Perré, P., Rémond, R., Almeida, G., Augusto, P. E. D., & Turner, I. (2023). State-of-the-art in the mechanistic modeling of the drying of solids: a review of 40 years of progress and perspectives. Drying Technology, 41( 6), 817–842. doi:10.1080/07373937.2022.2159974
NLM
Perré P, Rémond R, Almeida G, Augusto PED, Turner I. State-of-the-art in the mechanistic modeling of the drying of solids: a review of 40 years of progress and perspectives [Internet]. Drying Technology. 2023 ; 41( 6): 817–842.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1080/07373937.2022.2159974
Vancouver
Perré P, Rémond R, Almeida G, Augusto PED, Turner I. State-of-the-art in the mechanistic modeling of the drying of solids: a review of 40 years of progress and perspectives [Internet]. Drying Technology. 2023 ; 41( 6): 817–842.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1080/07373937.2022.2159974
Trabalho de evento
Intelligent diagnosis of engine failure in air vehicles using the ALFA dataset (2023)
Ferrão, Isadora Garcia ; Silva, Leandro Marcos da ; Silva, Sherlon Almeida da ; Dezan, Catherine ; Espes, David ; Branco, Kalinka Regina Lucas Jaquie Castelo
Source: Proceedings. Conference titles: International Conference on Unmanned Aircraft Systems - ICUAS. Unidade: ICMC
Subjects: AERONAVES NÃO TRIPULADAS, APRENDIZADO COMPUTACIONAL, CIDADES INTELIGENTES
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FERRÃO, Isadora Garcia et al. Intelligent diagnosis of engine failure in air vehicles using the ALFA dataset. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/ICUAS57906.2023.10156213. Acesso em: 29 jun. 2024.
APA
Ferrão, I. G., Silva, L. M. da, Silva, S. A. da, Dezan, C., Espes, D., & Branco, K. R. L. J. C. (2023). Intelligent diagnosis of engine failure in air vehicles using the ALFA dataset. In Proceedings. Piscataway: IEEE. doi:10.1109/ICUAS57906.2023.10156213
NLM
Ferrão IG, Silva LM da, Silva SA da, Dezan C, Espes D, Branco KRLJC. Intelligent diagnosis of engine failure in air vehicles using the ALFA dataset [Internet]. Proceedings. 2023 ;[citado 2024 jun. 29 ] Available from: https://doi.org/10.1109/ICUAS57906.2023.10156213
Vancouver
Ferrão IG, Silva LM da, Silva SA da, Dezan C, Espes D, Branco KRLJC. Intelligent diagnosis of engine failure in air vehicles using the ALFA dataset [Internet]. Proceedings. 2023 ;[citado 2024 jun. 29 ] Available from: https://doi.org/10.1109/ICUAS57906.2023.10156213
Trabalho de evento
Anomaly-based intrusion detection system for in-flight and network security in UAV swarm (2023)
Silva, Leandro Marcos da ; Ferrão, Isadora Garcia ; Dezan, Catherine ; Espes, David ; Branco, Kalinka Regina Lucas Jaquie Castelo
Source: Proceedings. Conference titles: International Conference on Unmanned Aircraft Systems - ICUAS. Unidade: ICMC
Subjects: AERONAVES NÃO TRIPULADAS, APRENDIZADO COMPUTACIONAL, ATAQUE, CIBERNÉTICA
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ABNT
SILVA, Leandro Marcos da et al. Anomaly-based intrusion detection system for in-flight and network security in UAV swarm. 2023, Anais.. Piscataway: IEEE, 2023. Disponível em: https://doi.org/10.1109/ICUAS57906.2023.10155873. Acesso em: 29 jun. 2024.
APA
Silva, L. M. da, Ferrão, I. G., Dezan, C., Espes, D., & Branco, K. R. L. J. C. (2023). Anomaly-based intrusion detection system for in-flight and network security in UAV swarm. In Proceedings. Piscataway: IEEE. doi:10.1109/ICUAS57906.2023.10155873
NLM
Silva LM da, Ferrão IG, Dezan C, Espes D, Branco KRLJC. Anomaly-based intrusion detection system for in-flight and network security in UAV swarm [Internet]. Proceedings. 2023 ;[citado 2024 jun. 29 ] Available from: https://doi.org/10.1109/ICUAS57906.2023.10155873
Vancouver
Silva LM da, Ferrão IG, Dezan C, Espes D, Branco KRLJC. Anomaly-based intrusion detection system for in-flight and network security in UAV swarm [Internet]. Proceedings. 2023 ;[citado 2024 jun. 29 ] Available from: https://doi.org/10.1109/ICUAS57906.2023.10155873
Artigo de periodico
Machine learning in marine ecology: an overview of techniques and applications (2023)
Rubbens, Peter ; Brodie, Stephanie ; Cordier, Tristan ; Barcellos, Diogo Destro ; Devos, Paul ; Fernandes-Salvador, Jose A. ; Fincham, Jennifer I. ; Gomes, Alessandra C. S.; Handegard, Nils Olav ; Howell, Kerry ; Jamet, Cédric ; Kartveit, Kyrre Heldal ; Moustahfid, Hassan ; Parcerisas, Clea ; Politikos, Dimitris ; Sauzède, Raphaëlle ; Sokolova, Maria ; Uusitalo, Laura ; Bulcke, Laure Van den ; Helmond, Aloysius T M van ; Watson, Jordan T. ; Welch, Heather ; Beltran-Perez, Oscar ; Chaffron, Samuel ; Greenberg, David S. ; Kühn, Bernhard; Kiko, Rainer ; Lo, Madiop; Lopes, Rubens M. ; Möller, Klas Ove ; Michaels, William ; Pala, Ahmet ; Romagnan, Jean-Baptiste ; Schuchert, Pia ; Seydi, Vahid ; Villasante, Sebastian ; Malde, Ketil ; Irisson, Jean-Olivier
Source: ICES Journal of Marine Science (print). Unidade: IO
Subjects: ACÚSTICA, ECOLOGIA, APRENDIZADO COMPUTACIONAL, SENSORIAMENTO REMOTO
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ABNT
RUBBENS, Peter et al. Machine learning in marine ecology: an overview of techniques and applications. ICES Journal of Marine Science (print), v. 80, n. 7, p. 1829-1853, 2023Tradução . . Disponível em: https://doi.org/10.1093/icesjms/fsad100. Acesso em: 29 jun. 2024.
APA
Rubbens, P., Brodie, S., Cordier, T., Barcellos, D. D., Devos, P., Fernandes-Salvador, J. A., et al. (2023). Machine learning in marine ecology: an overview of techniques and applications. ICES Journal of Marine Science (print), 80( 7), 1829-1853. doi:10.1093/icesjms/fsad100
NLM
Rubbens P, Brodie S, Cordier T, Barcellos DD, Devos P, Fernandes-Salvador JA, Fincham JI, Gomes ACS, Handegard NO, Howell K, Jamet C, Kartveit KH, Moustahfid H, Parcerisas C, Politikos D, Sauzède R, Sokolova M, Uusitalo L, Bulcke LV den, Helmond ATM van, Watson JT, Welch H, Beltran-Perez O, Chaffron S, Greenberg DS, Kühn B, Kiko R, Lo M, Lopes RM, Möller KO, Michaels W, Pala A, Romagnan J-B, Schuchert P, Seydi V, Villasante S, Malde K, Irisson J-O. Machine learning in marine ecology: an overview of techniques and applications [Internet]. ICES Journal of Marine Science (print). 2023 ; 80( 7): 1829-1853.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1093/icesjms/fsad100
Vancouver
Rubbens P, Brodie S, Cordier T, Barcellos DD, Devos P, Fernandes-Salvador JA, Fincham JI, Gomes ACS, Handegard NO, Howell K, Jamet C, Kartveit KH, Moustahfid H, Parcerisas C, Politikos D, Sauzède R, Sokolova M, Uusitalo L, Bulcke LV den, Helmond ATM van, Watson JT, Welch H, Beltran-Perez O, Chaffron S, Greenberg DS, Kühn B, Kiko R, Lo M, Lopes RM, Möller KO, Michaels W, Pala A, Romagnan J-B, Schuchert P, Seydi V, Villasante S, Malde K, Irisson J-O. Machine learning in marine ecology: an overview of techniques and applications [Internet]. ICES Journal of Marine Science (print). 2023 ; 80( 7): 1829-1853.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1093/icesjms/fsad100
Trabalho de evento
An experimental analysis of regression-obtained HPC scheduling heuristics (2023)
Rosa, Lucas de Sousa ; Carastan-Santos, Danilo ; Goldman, Alfredo
Source: Proceedings. Conference titles: Workshop on Job Scheduling Strategies for Parallel Processing - JSSPP. Unidade: IME
Subjects: HEURÍSTICA, SCHEDULING, APRENDIZADO COMPUTACIONAL, REGRESSÃO LINEAR
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ABNT
ROSA, Lucas de Sousa e CARASTAN-SANTOS, Danilo e GOLDMAN, Alfredo. An experimental analysis of regression-obtained HPC scheduling heuristics. 2023, Anais.. Cham: Springer, 2023. Disponível em: https://doi.org/10.1007/978-3-031-43943-8_6. Acesso em: 29 jun. 2024.
APA
Rosa, L. de S., Carastan-Santos, D., & Goldman, A. (2023). An experimental analysis of regression-obtained HPC scheduling heuristics. In Proceedings. Cham: Springer. doi:10.1007/978-3-031-43943-8_6
NLM
Rosa L de S, Carastan-Santos D, Goldman A. An experimental analysis of regression-obtained HPC scheduling heuristics [Internet]. Proceedings. 2023 ;[citado 2024 jun. 29 ] Available from: https://doi.org/10.1007/978-3-031-43943-8_6
Vancouver
Rosa L de S, Carastan-Santos D, Goldman A. An experimental analysis of regression-obtained HPC scheduling heuristics [Internet]. Proceedings. 2023 ;[citado 2024 jun. 29 ] Available from: https://doi.org/10.1007/978-3-031-43943-8_6
Trabalho de evento
Exploring simplicity and efficiency: regression-based scheduling heuristics in HPC (2023)
Rosa, Lucas de Sousa ; Carastan-Santos, Danilo ; Goldman, Alfredo
Source: Anais. Conference titles: Escola Regional de Alto Desempenho de São Paulo - ERAD-SP. Unidade: IME
Subjects: PROGRAMAÇÃO PARALELA, APRENDIZADO COMPUTACIONAL, HEURÍSTICA
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ABNT
ROSA, Lucas de Sousa e CARASTAN-SANTOS, Danilo e GOLDMAN, Alfredo. Exploring simplicity and efficiency: regression-based scheduling heuristics in HPC. 2023, Anais.. Porto Alegre: SBC, 2023. Disponível em: https://doi.org/10.5753/eradsp.2023.232635. Acesso em: 29 jun. 2024.
APA
Rosa, L. de S., Carastan-Santos, D., & Goldman, A. (2023). Exploring simplicity and efficiency: regression-based scheduling heuristics in HPC. In Anais. Porto Alegre: SBC. doi:10.5753/eradsp.2023.232635
NLM
Rosa L de S, Carastan-Santos D, Goldman A. Exploring simplicity and efficiency: regression-based scheduling heuristics in HPC [Internet]. Anais. 2023 ;[citado 2024 jun. 29 ] Available from: https://doi.org/10.5753/eradsp.2023.232635
Vancouver
Rosa L de S, Carastan-Santos D, Goldman A. Exploring simplicity and efficiency: regression-based scheduling heuristics in HPC [Internet]. Anais. 2023 ;[citado 2024 jun. 29 ] Available from: https://doi.org/10.5753/eradsp.2023.232635
Artigo de periodico
Neural architecture search with interpretable meta-features and fast predictors (2023)
Pereira, Gean Trindade ; Santos, Iury Batista de Andrade ; Garcia, Luís Paulo Faina ; Urruty, Thierry ; Visani, Muriel ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Information Sciences. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS
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ABNT
PEREIRA, Gean Trindade et al. Neural architecture search with interpretable meta-features and fast predictors. Information Sciences, v. No 2023, p. 1-17, 2023Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2023.119642. Acesso em: 29 jun. 2024.
APA
Pereira, G. T., Santos, I. B. de A., Garcia, L. P. F., Urruty, T., Visani, M., & Carvalho, A. C. P. de L. F. de. (2023). Neural architecture search with interpretable meta-features and fast predictors. Information Sciences, No 2023, 1-17. doi:10.1016/j.ins.2023.119642
NLM
Pereira GT, Santos IB de A, Garcia LPF, Urruty T, Visani M, Carvalho ACP de LF de. Neural architecture search with interpretable meta-features and fast predictors [Internet]. Information Sciences. 2023 ; No 2023 1-17.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1016/j.ins.2023.119642
Vancouver
Pereira GT, Santos IB de A, Garcia LPF, Urruty T, Visani M, Carvalho ACP de LF de. Neural architecture search with interpretable meta-features and fast predictors [Internet]. Information Sciences. 2023 ; No 2023 1-17.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1016/j.ins.2023.119642
Artigo de periodico
Learning curves for the multi-class teacher–student perceptron (2023)
Cornacchia, Elisabetta; Mignacco, Francesca; Veiga, Rodrigo ; Gerbelot, Cédric; Loureiro, Bruno; Zdeborová, Lenka
Source: Machine Learning: Science and Technology. Unidade: IF
Subjects: MECÂNICA ESTATÍSTICA, APRENDIZADO COMPUTACIONAL, INFERÊNCIA BAYESIANA, TEORIA DO RISCO, ALGORITMOS, INOVAÇÕES TECNOLÓGICAS
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ABNT
CORNACCHIA, Elisabetta et al. Learning curves for the multi-class teacher–student perceptron. Machine Learning: Science and Technology, v. 4, n. 1, p. 36 ; open access, 2023Tradução . . Disponível em: https://doi.org/10.1088/2632-2153/acb428. Acesso em: 29 jun. 2024.
APA
Cornacchia, E., Mignacco, F., Veiga, R., Gerbelot, C., Loureiro, B., & Zdeborová, L. (2023). Learning curves for the multi-class teacher–student perceptron. Machine Learning: Science and Technology, 4( 1), 36 ; open access. doi:10.1088/2632-2153/acb428
NLM
Cornacchia E, Mignacco F, Veiga R, Gerbelot C, Loureiro B, Zdeborová L. Learning curves for the multi-class teacher–student perceptron [Internet]. Machine Learning: Science and Technology. 2023 ; 4( 1): 36 ; open access.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1088/2632-2153/acb428
Vancouver
Cornacchia E, Mignacco F, Veiga R, Gerbelot C, Loureiro B, Zdeborová L. Learning curves for the multi-class teacher–student perceptron [Internet]. Machine Learning: Science and Technology. 2023 ; 4( 1): 36 ; open access.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1088/2632-2153/acb428
Artigo de periodico
A Deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from Brazil (2022)
Machicao, Jeaneth ; Specht, Alison; Vellenich, Danton ; Meneguzzi, Leandro ; David, Romain; Stall, Shelley; Ferraz, Katia ; Mabile, Laurence; O’Brien, Margaret; Corrêa, Pedro
Source: Data Science Journal. Unidades: EP, ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, COLETA DE DADOS, IMAGEM DIGITAL, INDICADORES SOCIOECONÔMICOS, REDES NEURAIS
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ABNT
MACHICAO, Jeaneth et al. A Deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from Brazil. Data Science Journal, v. 21, n. 6, p. 1–15, 2022Tradução . . Disponível em: https://doi.org/10.5334/dsj-2022-006. Acesso em: 29 jun. 2024.
APA
Machicao, J., Specht, A., Vellenich, D., Meneguzzi, L., David, R., Stall, S., et al. (2022). A Deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from Brazil. Data Science Journal, 21( 6), 1–15. doi:10.5334/dsj-2022-006
NLM
Machicao J, Specht A, Vellenich D, Meneguzzi L, David R, Stall S, Ferraz K, Mabile L, O’Brien M, Corrêa P. A Deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from Brazil [Internet]. Data Science Journal. 2022 ; 21( 6): 1–15.[citado 2024 jun. 29 ] Available from: https://doi.org/10.5334/dsj-2022-006
Vancouver
Machicao J, Specht A, Vellenich D, Meneguzzi L, David R, Stall S, Ferraz K, Mabile L, O’Brien M, Corrêa P. A Deep-learning method for the prediction of socio-economic indicators from street-view imagery using a case study from Brazil [Internet]. Data Science Journal. 2022 ; 21( 6): 1–15.[citado 2024 jun. 29 ] Available from: https://doi.org/10.5334/dsj-2022-006
Artigo de periodico
A divide‑and‑conquer approach for genomic prediction in rubber tree using machine learning (2022)
Aono, Alexandre Hild; Francisco, Felipe Roberto; Souza, Livia Moura; Gonçalves, Paulo de Souza; Scaloppi Junior, Erivaldo J; Le Guen, Vincent; Fritsche‑Neto, Roberto ; Gorjanc, Gregor; Marcos Gonçalves Quiles8; Souza, Anete Pereira de
Source: Scientific Reports. Unidade: ESALQ
Subjects: APRENDIZADO COMPUTACIONAL, GENÔMICA, MARCADOR MOLECULAR, MELHORAMENTO GENÉTICO VEGETAL, REDES NEURAIS, SERINGUEIRA
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ABNT
AONO, Alexandre Hild et al. A divide‑and‑conquer approach for genomic prediction in rubber tree using machine learning. Scientific Reports, v. 12, p. 1-14, 2022Tradução . . Disponível em: https://repositorio.usp.br/directbitstream/86d8ec26-96c6-424e-a56e-717547051442/3105258-A%20divide-and-conquer%20approach%20for%20genomic%20prediction%20in%20rubber%20tree%20using%20machine%20learning.pdf. Acesso em: 29 jun. 2024.
APA
Aono, A. H., Francisco, F. R., Souza, L. M., Gonçalves, P. de S., Scaloppi Junior, E. J., Le Guen, V., et al. (2022). A divide‑and‑conquer approach for genomic prediction in rubber tree using machine learning. Scientific Reports, 12, 1-14. doi:10.1038/s41598-022-20416-z
NLM
Aono AH, Francisco FR, Souza LM, Gonçalves P de S, Scaloppi Junior EJ, Le Guen V, Fritsche‑Neto R, Gorjanc G, Marcos Gonçalves Quiles8, Souza AP de. A divide‑and‑conquer approach for genomic prediction in rubber tree using machine learning [Internet]. Scientific Reports. 2022 ; 12 1-14.[citado 2024 jun. 29 ] Available from: https://repositorio.usp.br/directbitstream/86d8ec26-96c6-424e-a56e-717547051442/3105258-A%20divide-and-conquer%20approach%20for%20genomic%20prediction%20in%20rubber%20tree%20using%20machine%20learning.pdf
Vancouver
Aono AH, Francisco FR, Souza LM, Gonçalves P de S, Scaloppi Junior EJ, Le Guen V, Fritsche‑Neto R, Gorjanc G, Marcos Gonçalves Quiles8, Souza AP de. A divide‑and‑conquer approach for genomic prediction in rubber tree using machine learning [Internet]. Scientific Reports. 2022 ; 12 1-14.[citado 2024 jun. 29 ] Available from: https://repositorio.usp.br/directbitstream/86d8ec26-96c6-424e-a56e-717547051442/3105258-A%20divide-and-conquer%20approach%20for%20genomic%20prediction%20in%20rubber%20tree%20using%20machine%20learning.pdf
Artigo de periodico
CPAP adherence assessment via gaussian mixture modeling of telemonitored apnea therapy (2022)
Rodrigues Junior, José Fernando ; Bailly, Sebastien ; Pepin, Jean-Louis ; Goeuriot, Lorraine ; Spadon, Gabriel ; Amer-Yahia, Sihem
Source: Applied Sciences. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, ANÁLISE DE SÉRIES TEMPORAIS, TECNOLOGIAS DA SAÚDE, TRANSTORNOS DO SONO, APNEIA DO SONO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RODRIGUES JUNIOR, José Fernando et al. CPAP adherence assessment via gaussian mixture modeling of telemonitored apnea therapy. Applied Sciences, v. 12, n. 15, p. 1-17, 2022Tradução . . Disponível em: https://doi.org/10.3390/app12157618. Acesso em: 29 jun. 2024.
APA
Rodrigues Junior, J. F., Bailly, S., Pepin, J. -L., Goeuriot, L., Spadon, G., & Amer-Yahia, S. (2022). CPAP adherence assessment via gaussian mixture modeling of telemonitored apnea therapy. Applied Sciences, 12( 15), 1-17. doi:10.3390/app12157618
NLM
Rodrigues Junior JF, Bailly S, Pepin J-L, Goeuriot L, Spadon G, Amer-Yahia S. CPAP adherence assessment via gaussian mixture modeling of telemonitored apnea therapy [Internet]. Applied Sciences. 2022 ; 12( 15): 1-17.[citado 2024 jun. 29 ] Available from: https://doi.org/10.3390/app12157618
Vancouver
Rodrigues Junior JF, Bailly S, Pepin J-L, Goeuriot L, Spadon G, Amer-Yahia S. CPAP adherence assessment via gaussian mixture modeling of telemonitored apnea therapy [Internet]. Applied Sciences. 2022 ; 12( 15): 1-17.[citado 2024 jun. 29 ] Available from: https://doi.org/10.3390/app12157618
Artigo de periodico
Diffuse reflectance spectroscopy for estimating soil properties: a technology for the 21st century (2022)
Viscarra Rossel, Raphael A; Behrens, Thorsten; Ben‐Dor, Eyal; Chabrillat, Sabine; Demattê, José Alexandre Melo ; Ge, Yufeng; Gomez, Cecile; Guerrero, César; Peng, Yi; Ramirez‐Lopez, Leonardo; Shi, Zhou; Stenberg, Bo; Webster, Richard; Winowiecki, Leigh; Shen, Zefang
Source: European Journal of Soil Science. Unidade: ESALQ
Subjects: ANÁLISE DO SOLO, APRENDIZADO COMPUTACIONAL, ESPECTROSCOPIA, SOLOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
VISCARRA ROSSEL, Raphael A et al. Diffuse reflectance spectroscopy for estimating soil properties: a technology for the 21st century. European Journal of Soil Science, v. 73, p. 1-9, 2022Tradução . . Disponível em: https://doi.org/10.1111/ejss.13271. Acesso em: 29 jun. 2024.
APA
Viscarra Rossel, R. A., Behrens, T., Ben‐Dor, E., Chabrillat, S., Demattê, J. A. M., Ge, Y., et al. (2022). Diffuse reflectance spectroscopy for estimating soil properties: a technology for the 21st century. European Journal of Soil Science, 73, 1-9. doi:10.1111/ejss.13271
NLM
Viscarra Rossel RA, Behrens T, Ben‐Dor E, Chabrillat S, Demattê JAM, Ge Y, Gomez C, Guerrero C, Peng Y, Ramirez‐Lopez L, Shi Z, Stenberg B, Webster R, Winowiecki L, Shen Z. Diffuse reflectance spectroscopy for estimating soil properties: a technology for the 21st century [Internet]. European Journal of Soil Science. 2022 ; 73 1-9.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1111/ejss.13271
Vancouver
Viscarra Rossel RA, Behrens T, Ben‐Dor E, Chabrillat S, Demattê JAM, Ge Y, Gomez C, Guerrero C, Peng Y, Ramirez‐Lopez L, Shi Z, Stenberg B, Webster R, Winowiecki L, Shen Z. Diffuse reflectance spectroscopy for estimating soil properties: a technology for the 21st century [Internet]. European Journal of Soil Science. 2022 ; 73 1-9.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1111/ejss.13271
Artigo de periodico
A profile-based AI-assisted dynamic scheduling approach for heterogeneous architectures (2022)
Geng, Tongsheng ; Amarís, Marcos ; Zuckerman, Stéphane ; Goldman, Alfredo ; Gao, Guang R.; Gaudiot, Jean-Luc
Source: International Journal of Parallel Programming. Unidade: IME
Subjects: APRENDIZADO COMPUTACIONAL, SCHEDULING
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
GENG, Tongsheng et al. A profile-based AI-assisted dynamic scheduling approach for heterogeneous architectures. International Journal of Parallel Programming, v. 50, n. 1, p. 115-151, 2022Tradução . . Disponível em: https://doi.org/10.1007/s10766-021-00721-2. Acesso em: 29 jun. 2024.
APA
Geng, T., Amarís, M., Zuckerman, S., Goldman, A., Gao, G. R., & Gaudiot, J. -L. (2022). A profile-based AI-assisted dynamic scheduling approach for heterogeneous architectures. International Journal of Parallel Programming, 50( 1), 115-151. doi:10.1007/s10766-021-00721-2
NLM
Geng T, Amarís M, Zuckerman S, Goldman A, Gao GR, Gaudiot J-L. A profile-based AI-assisted dynamic scheduling approach for heterogeneous architectures [Internet]. International Journal of Parallel Programming. 2022 ; 50( 1): 115-151.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1007/s10766-021-00721-2
Vancouver
Geng T, Amarís M, Zuckerman S, Goldman A, Gao GR, Gaudiot J-L. A profile-based AI-assisted dynamic scheduling approach for heterogeneous architectures [Internet]. International Journal of Parallel Programming. 2022 ; 50( 1): 115-151.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1007/s10766-021-00721-2
Trabalho de evento-anais periodico
YourTTS: towards zero-shot multi-speaker TTS and zero-shot voice conversion for everyone (2022)
Casanova, Edresson ; Weber, Julian; Shulby, Christopher Dane ; Candido Junior, Arnaldo ; Gölge, Eren; Ponti, Moacir Antonelli
Source: Proceedings of Machine Learning Research : PMLR. Conference titles: International Conference on Machine Learning - ICML. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, PROCESSAMENTO DE LINGUAGEM NATURAL, RECONHECIMENTO DA FALA, SÍNTESE DE FALA
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
CASANOVA, Edresson et al. YourTTS: towards zero-shot multi-speaker TTS and zero-shot voice conversion for everyone. Proceedings of Machine Learning Research : PMLR. Brookline: Microtome Publishing. Disponível em: https://proceedings.mlr.press/v162/casanova22a.html. Acesso em: 29 jun. 2024. , 2022
APA
Casanova, E., Weber, J., Shulby, C. D., Candido Junior, A., Gölge, E., & Ponti, M. A. (2022). YourTTS: towards zero-shot multi-speaker TTS and zero-shot voice conversion for everyone. Proceedings of Machine Learning Research : PMLR. Brookline: Microtome Publishing. Recuperado de https://proceedings.mlr.press/v162/casanova22a.html
NLM
Casanova E, Weber J, Shulby CD, Candido Junior A, Gölge E, Ponti MA. YourTTS: towards zero-shot multi-speaker TTS and zero-shot voice conversion for everyone [Internet]. Proceedings of Machine Learning Research : PMLR. 2022 ; 162 2709-2720.[citado 2024 jun. 29 ] Available from: https://proceedings.mlr.press/v162/casanova22a.html
Vancouver
Casanova E, Weber J, Shulby CD, Candido Junior A, Gölge E, Ponti MA. YourTTS: towards zero-shot multi-speaker TTS and zero-shot voice conversion for everyone [Internet]. Proceedings of Machine Learning Research : PMLR. 2022 ; 162 2709-2720.[citado 2024 jun. 29 ] Available from: https://proceedings.mlr.press/v162/casanova22a.html
Artigo de periodico
LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks (2021)
Rodrigues Junior, José Fernando ; Gutierrez, Marco Antonio ; Spadon, Gabriel ; Machado, Bruno Brandoli ; Amer-Yahia, Sihem
Source: Information Sciences. Unidades: ICMC, EP
Subjects: APRENDIZADO COMPUTACIONAL, REDES NEURAIS, PREVISÃO (ANÁLISE DE SÉRIES TEMPORAIS), PROGNÓSTICO, TECNOLOGIAS DA SAÚDE
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
RODRIGUES JUNIOR, José Fernando et al. LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks. Information Sciences, v. 545, p. 813-827, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2020.09.024. Acesso em: 29 jun. 2024.
APA
Rodrigues Junior, J. F., Gutierrez, M. A., Spadon, G., Machado, B. B., & Amer-Yahia, S. (2021). LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks. Information Sciences, 545, 813-827. doi:10.1016/j.ins.2020.09.024
NLM
Rodrigues Junior JF, Gutierrez MA, Spadon G, Machado BB, Amer-Yahia S. LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks [Internet]. Information Sciences. 2021 ; 545 813-827.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1016/j.ins.2020.09.024
Vancouver
Rodrigues Junior JF, Gutierrez MA, Spadon G, Machado BB, Amer-Yahia S. LIG-Doctor: efficient patient trajectory prediction using bidirectional minimal gated-recurrent networks [Internet]. Information Sciences. 2021 ; 545 813-827.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1016/j.ins.2020.09.024
Artigo de periodico
Assessing the data complexity of imbalanced datasets (2021)
Barella, Victor Hugo ; Garcia, Luís Paulo Faina ; Souto, Marcílio Carlos Pereira de ; Lorena, Ana Carolina ; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Information Sciences. Unidade: ICMC
Subjects: APRENDIZADO COMPUTACIONAL, RECONHECIMENTO DE PADRÕES, MATEMÁTICA DA COMPUTAÇÃO
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BARELLA, Victor Hugo et al. Assessing the data complexity of imbalanced datasets. Information Sciences, v. 553, p. 83-109, 2021Tradução . . Disponível em: https://doi.org/10.1016/j.ins.2020.12.006. Acesso em: 29 jun. 2024.
APA
Barella, V. H., Garcia, L. P. F., Souto, M. C. P. de, Lorena, A. C., & Carvalho, A. C. P. de L. F. de. (2021). Assessing the data complexity of imbalanced datasets. Information Sciences, 553, 83-109. doi:10.1016/j.ins.2020.12.006
NLM
Barella VH, Garcia LPF, Souto MCP de, Lorena AC, Carvalho ACP de LF de. Assessing the data complexity of imbalanced datasets [Internet]. Information Sciences. 2021 ; 553 83-109.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1016/j.ins.2020.12.006
Vancouver
Barella VH, Garcia LPF, Souto MCP de, Lorena AC, Carvalho ACP de LF de. Assessing the data complexity of imbalanced datasets [Internet]. Information Sciences. 2021 ; 553 83-109.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1016/j.ins.2020.12.006
Trabalho de evento-anais periodico
Evaluate pseudo labeling and CNN for multi-variate time series classification in low-data regimes (2021)
Ienco, Dino ; Pereira-Santos, Davi; Carvalho, André Carlos Ponce de Leon Ferreira de
Source: Lecture Notes in Computer Science. Conference titles: International Conference on Artificial Neural Networks - ICANN. Unidade: ICMC
Subjects: ANÁLISE DE SÉRIES TEMPORAIS, REDES NEURAIS, APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
IENCO, Dino e PEREIRA-SANTOS, Davi e CARVALHO, André Carlos Ponce de Leon Ferreira de. Evaluate pseudo labeling and CNN for multi-variate time series classification in low-data regimes. Lecture Notes in Computer Science. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-030-86383-8_10. Acesso em: 29 jun. 2024. , 2021
APA
Ienco, D., Pereira-Santos, D., & Carvalho, A. C. P. de L. F. de. (2021). Evaluate pseudo labeling and CNN for multi-variate time series classification in low-data regimes. Lecture Notes in Computer Science. Cham: Springer. doi:10.1007/978-3-030-86383-8_10
NLM
Ienco D, Pereira-Santos D, Carvalho ACP de LF de. Evaluate pseudo labeling and CNN for multi-variate time series classification in low-data regimes [Internet]. Lecture Notes in Computer Science. 2021 ; 12895 126-137.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1007/978-3-030-86383-8_10
Vancouver
Ienco D, Pereira-Santos D, Carvalho ACP de LF de. Evaluate pseudo labeling and CNN for multi-variate time series classification in low-data regimes [Internet]. Lecture Notes in Computer Science. 2021 ; 12895 126-137.[citado 2024 jun. 29 ] Available from: https://doi.org/10.1007/978-3-030-86383-8_10

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