Abstract is missing.
- DiffML: End-to-end Differentiable ML PipelinesBenjamin Hilprecht, Christian Hammacher, Eduardo Reis, Mohamed Abdelaal 0001, Carsten Binnig. [doi]
- Data Management and Visualization for Benchmarking Deep Learning Training SystemsTies Robroek, Aaron Duane, Ehsan Yousefzadeh-Asl-Miandoab, Pinar Tözün. [doi]
- Transactional Python for Durable Machine Learning: Vision, Challenges, and FeasibilitySupawit Chockchowwat, Zhaoheng Li, Yongjoo Park. [doi]
- P2D: A Transpiler Framework for Optimizing Data Science PipelinesYordan Grigorov, Haralampos Gavriilidis, Sergey Redyuk, Kaustubh Beedkar, Volker Markl. [doi]
- Teaching Blue Elephants the Maths for Machine LearningClemens Ruck, Maximilian Emanuel Schüle. [doi]
- Using Pipeline Performance Prediction to Accelerate AutoML SystemsHaoxiang Zhang, Roque Lopez, Aécio S. R. Santos, Jorge Piazentin Ono, Aline Bessa, Juliana Freire. [doi]
- MLflow2PROV: Extracting Provenance from Machine Learning ExperimentsMarius Schlegel, Kai-Uwe Sattler. [doi]
- EVA: An End-to-End Exploratory Video Analytics SystemGaurav Tarlok Kakkar, Jiashen Cao, Pramod Chunduri, Zhuangdi Xu, Suryatej Reddy Vyalla, Prashanth Dintyala, Anirudh Prabakaran, Jaeho Bang, Aubhro Sengupta, Kaushik Ravichandran 0001, 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. [doi]
- When Can We Ignore Missing Data in Model Training?Cheng Zhen, Amandeep Singh Chabada, Arash Termehchy. [doi]