Abstract is missing.
- Towards data-centric what-if analysis for native machine learning pipelinesStefan Grafberger, Paul Groth, Sebastian Schelter. [doi]
- dcbench: a benchmark for data-centric AI systemsSabri Eyuboglu, Bojan Karlas, Christopher Ré, Ce Zhang 0001, James Zou 0001. [doi]
- Learning-to-learn efficiently with self-learningShruti Kunde, Sharod Roy Choudhury, Amey Pandit, Rekha Singhal. [doi]
- LLVM code optimisation for automatic differentiation: when forward and reverse mode lead in the same directionMaximilian E. Schüle, Maximilian Springer, Alfons Kemper, Thomas Neumann 0001. [doi]
- Minun: evaluating counterfactual explanations for entity matchingJin Wang, Yuliang Li 0001. [doi]
- Evaluating model serving strategies over streaming dataSonia Horchidan, Emmanouil Kritharakis, Vasiliki Kalavri, Paris Carbone. [doi]
- Accelerating container-based deep learning hyperparameter optimization workloadsRui Liu, David Wong, Dave Lange, Patrik Larsson, Vinay Jethava, Qing Zheng. [doi]
- How I stopped worrying about training data bugs and started complainingLampros Flokas, Weiyuan Wu, Jiannan Wang, Nakul Verma, Eugene Wu 0002. [doi]
- GouDa - generation of universal data sets: improving analysis and evaluation of data preparation pipelinesValerie Restat, Gerrit Boerner, André Conrad, Uta Störl. [doi]