ReP USP - Resultado da busca

Filtros : "Conference on Neural Information Processing Systems - NeurIPS" Removido: "BARROS, LELIANE NUNES DE" Limpar

Filtros

Trabalho de evento
A compositional atlas for algebraic circuits (2024)
Wang, Bin ; Mauá, Denis Deratani ; Van den Broeck, Guy; Choi, YooJung
Fonte: Proceedings. Nome do evento: Conference on Neural Information Processing Systems - NeurIPS. Unidade: IME
Assuntos: INTELIGÊNCIA ARTIFICIAL, MODELOS PARA PROCESSOS ESTOCÁSTICOS
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
WANG, Bin et al. A compositional atlas for algebraic circuits. 2024, Anais.. San Diego: NeurIPS, 2024. Disponível em: https://proceedings.neurips.cc/paper_files/paper/2024/file/ff9c70659c39cdd801dd5f5a1201c29e-Paper-Conference.pdf. Acesso em: 07 nov. 2025.
APA
Wang, B., Mauá, D. D., Van den Broeck, G., & Choi, Y. J. (2024). A compositional atlas for algebraic circuits. In Proceedings. San Diego: NeurIPS. Recuperado de https://proceedings.neurips.cc/paper_files/paper/2024/file/ff9c70659c39cdd801dd5f5a1201c29e-Paper-Conference.pdf
NLM
Wang B, Mauá DD, Van den Broeck G, Choi YJ. A compositional atlas for algebraic circuits [Internet]. Proceedings. 2024 ;[citado 2025 nov. 07 ] Available from: https://proceedings.neurips.cc/paper_files/paper/2024/file/ff9c70659c39cdd801dd5f5a1201c29e-Paper-Conference.pdf
Vancouver
Wang B, Mauá DD, Van den Broeck G, Choi YJ. A compositional atlas for algebraic circuits [Internet]. Proceedings. 2024 ;[citado 2025 nov. 07 ] Available from: https://proceedings.neurips.cc/paper_files/paper/2024/file/ff9c70659c39cdd801dd5f5a1201c29e-Paper-Conference.pdf
Trabalho de evento-anais periodico
Lessons learned from the NeurIPS 2021 MetaDL challenge: backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification (2022)
Baz, Adrian El; Ullah, Ihsan; Alcobaça, Edesio ; Carvalho, André Carlos Ponce de Leon Ferreira de ; Chen, Hong; Ferreira, Fábio; Gouk, Henry; Guan, Chaoyu; Guyon, Isabelle; Hospedales, Timothy; Hu, Shell; Huisman, Mike ; Hutter, Frank ; Liu, Zhengying; Mohr, Felix ; Öztürk, Ekrem ; Rijn, Jan N. van ; Sun, Haozhe; Wang, Xin; Zhu, Wenwu
Fonte: Proceedings of Machine Learning Research : PMLR. Nome do evento: Conference on Neural Information Processing Systems - NeurIPS. Unidade: ICMC
Assunto: APRENDIZADO COMPUTACIONAL
A citação é gerada automaticamente e pode não estar totalmente de acordo com as normas
ABNT
BAZ, Adrian El et al. Lessons learned from the NeurIPS 2021 MetaDL challenge: backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. Proceedings of Machine Learning Research : PMLR. Brookline: Microtome Publishing. Disponível em: https://proceedings.mlr.press/v176/el-baz22a.html. Acesso em: 07 nov. 2025. , 2022
APA
Baz, A. E., Ullah, I., Alcobaça, E., Carvalho, A. C. P. de L. F. de, Chen, H., Ferreira, F., et al. (2022). Lessons learned from the NeurIPS 2021 MetaDL challenge: backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification. Proceedings of Machine Learning Research : PMLR. Brookline: Microtome Publishing. Recuperado de https://proceedings.mlr.press/v176/el-baz22a.html
NLM
Baz AE, Ullah I, Alcobaça E, Carvalho ACP de LF de, Chen H, Ferreira F, Gouk H, Guan C, Guyon I, Hospedales T, Hu S, Huisman M, Hutter F, Liu Z, Mohr F, Öztürk E, Rijn JN van, Sun H, Wang X, Zhu W. Lessons learned from the NeurIPS 2021 MetaDL challenge: backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification [Internet]. Proceedings of Machine Learning Research : PMLR. 2022 ; 176 80-96.[citado 2025 nov. 07 ] Available from: https://proceedings.mlr.press/v176/el-baz22a.html
Vancouver
Baz AE, Ullah I, Alcobaça E, Carvalho ACP de LF de, Chen H, Ferreira F, Gouk H, Guan C, Guyon I, Hospedales T, Hu S, Huisman M, Hutter F, Liu Z, Mohr F, Öztürk E, Rijn JN van, Sun H, Wang X, Zhu W. Lessons learned from the NeurIPS 2021 MetaDL challenge: backbone fine-tuning without episodic meta-learning dominates for few-shot learning image classification [Internet]. Proceedings of Machine Learning Research : PMLR. 2022 ; 176 80-96.[citado 2025 nov. 07 ] Available from: https://proceedings.mlr.press/v176/el-baz22a.html

Biblioteca Digital de Produção Intelectual da Universidade de São Paulo 2012 - 2025

Unidades USP

ReP

Filtros

Autores

Agência de fomento