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ORCID6th DEEM@SIGMOD 2023: Seattle, WA, USA
- Proceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning, DEEM 2023, Seattle, WA, USA, 18 June 2023. ACM 2023
- Ties Robroek
, Aaron Duane, Ehsan Yousefzadeh-Asl-Miandoab, Pinar Tözün:
Data Management and Visualization for Benchmarking Deep Learning Training Systems. 1:1-1:5 - Clemens Ruck, Maximilian Emanuel Schüle:
Teaching Blue Elephants the Maths for Machine Learning. 2:1-2:4 - Yordan Grigorov, Haralampos Gavriilidis, Sergey Redyuk, Kaustubh Beedkar, Volker Markl:
P2D: A Transpiler Framework for Optimizing Data Science Pipelines. 3:1-3:4 - Cheng Zhen, Amandeep Singh Chabada, Arash Termehchy:
When Can We Ignore Missing Data in Model Training? 4:1-4:4 - Supawit Chockchowwat, Zhaoheng Li, Yongjoo Park:
Transactional Python for Durable Machine Learning: Vision, Challenges, and Feasibility. 5:1-5:5 - Haoxiang Zhang, Roque Lopez, Aécio S. R. Santos, Jorge Piazentin Ono, Aline Bessa, Juliana Freire:
Using Pipeline Performance Prediction to Accelerate AutoML Systems. 6:1-6:11 - Benjamin Hilprecht, Christian Hammacher, Eduardo Souza dos Reis, Mohamed Abdelaal, Carsten Binnig:
DiffML: End-to-end Differentiable ML Pipelines. 7:1-7:7 - Gaurav Tarlok Kakkar, Jiashen Cao, Pramod Chunduri, Zhuangdi Xu, Suryatej Reddy Vyalla, Prashanth Dintyala, Anirudh Prabakaran, Jaeho Bang, Aubhro Sengupta, Kaushik Ravichandran, Ishwarya Sivakumar, Aryan Rajoria, Ashmita Raju, Tushar Aggarwal, Abdullah Shah, Sanjana Garg, Shashank Suman, Myna Prasanna Kalluraya, Subrata Mitra, Ali Payani, Yao Lu, Umakishore Ramachandran, Joy Arulraj:
EVA: An End-to-End Exploratory Video Analytics System. 8:1-8:5 - Marius Schlegel, Kai-Uwe Sattler:
MLflow2PROV: Extracting Provenance from Machine Learning Experiments. 9:1-9:4
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