SBBD

Paper Registration

1

Select Book

2

Select Paper

3

Fill in paper information

4

Congratulations

Fill in your paper information

English Information

(*) To change the order drag the item to the new position.

Authors
# Name
1 Lyncoln de Oliveira(oliveiral@cos.ufrj.br)
2 Rômulo Silva(romulo@coc.ufrj.br)
3 Liliane Kunstmann(lneves@cos.ufrj.br)
4 Débora Pina(dbpina@cos.ufrj.br)
5 Daniel de Oliveira(danielcmo@ic.uff.br)
6 Alvaro Coutinho(alvaro@coc.ufrj.br)
7 Marta Mattoso(marta@cos.ufrj.br)

(*) To change the order drag the item to the new position.

Reference
# Reference
1 Debnath, L. (2012). First-Order Nonlinear Equations and Their Applications, pages 227–256. Birkhäuser Boston, Boston.
2 Karmaker, S. K., Hassan, M. M., Smith, M. J., Xu, L., Zhai, C., and Veeramachaneni, K. (2021). Automl to date and beyond: Challenges and opportunities. ACM Comput. Surv.,54(8):1–36.
3 Kidger, P. and Lyons, T. (2020). Universal approximation with deep narrow networks. In Conference on learning theory, pages 2306–2327.
4 Kumar, A., Nakandala, S., Zhang, Y., Li, S., Gemawat, A., and Nagrecha, K. (2021). Cerebro: A layered data platform for scalable deep learning. In 11th Annual Conference on Innovative Data Systems Research (CIDR’21).
5 Moreau, L. and Groth, P. (2013). Provenance: An Introduction to PROV. Synthesis Lectures on the Semantic Web: Theory and Technology. Morgan & Claypool Publishers.
6 Pina, D., Neves, L., de Oliveira, D., and Mattoso, M. (2021). Captura automática de dados de proveniência de experimentos de aprendizado de máquina com keras-prov. In Anais Estendidos do XXXVI SBBD, pages 69–74. SBC.
7 Raissi, M., Perdikaris, P., and Karniadakis, G. E. (2019). Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational physics, 378:686–707.
8 Silva, R., Pina, D., Kunstmann, L., de Oliveira, D., Valduriez, P., Coutinho, A., and Mattoso, M. (2021). Capturing provenance to improve the model training of pinns: first hand-on experiences with grid5000. In 42nd CILAMCE, pages 1–7.
9 Silva, R. M. and Coutinho, A. L. (2020). Physics–informed neural networks for the factored eikonal equation. In 41nd CILAMCE.
10 Vartak, M., Subramanyam, H., Lee, W.-E., Viswanathan, S., Husnoo, S., Madden, S., and Zaharia, M. (2016). Modeldb: a system for machine learning model management. In Proceedings of the Workshop on Human-In-the-Loop Data Analytics, pages 1–3.
11 Wang, D., Weisz, J. D., Muller, M., Ram, P., Geyer, W., Dugan, C., Tausczik, Y., Samulowitz, H., and Gray, A. (2019). Human-ai collaboration in data science: Exploring data scientists’ perceptions of automated ai. Proceedings of the ACM on Human-Computer Interaction, 3(CSCW):1–24.