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ORCID6th DEEM@SIGMOD 2022: Philadelphia, PA, USA
- Matthias Boehm, Paroma Varma, Doris Xin:
DEEM '22: Proceedings of the Sixth Workshop on Data Management for End-To-End Machine Learning Philadelphia, PA, USA, 12 June 2022. ACM 2022, ISBN 978-1-4503-9375-1 - Lampros Flokas, Weiyuan Wu, Jiannan Wang, Nakul Verma, Eugene Wu:
How I stopped worrying about training data bugs and started complaining. 1:1-1:5 - Valerie Restat, Gerrit Boerner, André Conrad, Uta Störl:
GouDa - generation of universal data sets: improving analysis and evaluation of data preparation pipelines. 2:1-2:6 - Stefan Grafberger
, Paul Groth, Sebastian Schelter:
Towards data-centric what-if analysis for native machine learning pipelines. 3:1-3:5 - Sonia Horchidan, Emmanouil Kritharakis, Vasiliki Kalavri, Paris Carbone:
Evaluating model serving strategies over streaming data. 4:1-4:5 - Maximilian E. Schüle, Maximilian Springer, Alfons Kemper, Thomas Neumann:
LLVM code optimisation for automatic differentiation: when forward and reverse mode lead in the same direction. 5:1-5:4 - Rui Liu, David Wong, Dave Lange, Patrik Larsson, Vinay Jethava, Qing Zheng:
Accelerating container-based deep learning hyperparameter optimization workloads. 6:1-6:10 - Jin Wang, Yuliang Li:
Minun: evaluating counterfactual explanations for entity matching. 7:1-7:11 - Shruti Kunde, Sharod Roy Choudhury, Amey Pandit, Rekha Singhal:
Learning-to-learn efficiently with self-learning. 8:1-8:11 - Sabri Eyuboglu, Bojan Karlas, Christopher Ré, Ce Zhang, James Zou:
dcbench: a benchmark for data-centric AI systems. 9:1-9:4
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