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Publication search results
found 78 matches
- 2024
- Mubashara Akhtar, Omar Benjelloun, Costanza Conforti, Pieter Gijsbers, Joan Giner-Miguelez, Nitisha Jain, Michael Kuchnik, Quentin Lhoest, Pierre Marcenac, Manil Maskey, Peter Mattson, Luis Oala, Pierre Ruyssen, Rajat Shinde, Elena Simperl, Goeffry Thomas, Slava Tykhonov, Joaquin Vanschoren, Jos van der Velde, Steffen Vogler, Carole-Jean Wu:
Croissant: A Metadata Format for ML-Ready Datasets. DEEM@SIGMOD 2024: 1-6 - Robert Bayer, Julian Priest, Pinar Tözün:
Reaching the Edge of the Edge: Image Analysis in Space. DEEM@SIGMOD 2024: 29-38 - Timothy Dai, Austin Peters, Jonah B. Gelbach, David Freeman Engstrom, Daniel Kang:
tailwiz: Empowering Domain Experts with Easy-to-Use, Task-Specific Natural Language Processing Models. DEEM@SIGMOD 2024: 12-22 - Stefan Grafberger, Paul Groth, Sebastian Schelter:
Towards Interactively Improving ML Data Preparation Code via "Shadow Pipelines". DEEM@SIGMOD 2024: 7-11 - Tengjun Jin, Akash Mittal, Chenghao Mo, Jiahao Fang, Chengsong Zhang, Timothy Dai, Daniel Kang:
AIDB: a Sparsely Materialized Database for Queries using Machine Learning. DEEM@SIGMOD 2024: 23-28 - Abhilash Jindal, Kaustubh Beedkar, Vishal Singh, J. Nausheen Mohammed, Tushar Singla, Aman Gupta, Keerti Choudhary:
Reactive Dataflow for Inflight Error Handling in ML Workflows. DEEM@SIGMOD 2024: 51-61 - Konstantinos Kanellis, Johannes Freischuetz, Shivaram Venkataraman:
Nautilus: A Benchmarking Platform for DBMS Knob Tuning. DEEM@SIGMOD 2024: 72-76 - Xue Li, Till Döhmen:
Towards Efficient Data Wrangling with LLMs using Code Generation. DEEM@SIGMOD 2024: 62-66 - Débora B. Pina, Adriane Chapman, Liliane N. O. Kunstmann, Daniel de Oliveira, Marta Mattoso:
DLProv: A Data-Centric Support for Deep Learning Workflow Analyses. DEEM@SIGMOD 2024: 77-85 - Jacopo Tagliabue, Ciro Greco:
Reproducible data science over data lakes: replayable data pipelines with Bauplan and Nessie. DEEM@SIGMOD 2024: 67-71 - Herbert Woisetschläger, Alexander Erben, Shiqiang Wang, Ruben Mayer, Hans-Arno Jacobsen:
Federated Fine-Tuning of LLMs on the Very Edge: The Good, the Bad, the Ugly. DEEM@SIGMOD 2024: 39-50 - Proceedings of the Eighth Workshop on Data Management for End-to-End Machine Learning, DEEM 2024, Santiago, AA, Chile, 9 June 2024. ACM 2024 [contents]
- 2023
- Supawit Chockchowwat, Zhaoheng Li, Yongjoo Park:
Transactional Python for Durable Machine Learning: Vision, Challenges, and Feasibility. DEEM@SIGMOD 2023: 5:1-5:5 - Yordan Grigorov, Haralampos Gavriilidis, Sergey Redyuk, Kaustubh Beedkar, Volker Markl:
P2D: A Transpiler Framework for Optimizing Data Science Pipelines. DEEM@SIGMOD 2023: 3:1-3:4 - Benjamin Hilprecht, Christian Hammacher, Eduardo Souza dos Reis, Mohamed Abdelaal, Carsten Binnig:
DiffML: End-to-end Differentiable ML Pipelines. DEEM@SIGMOD 2023: 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. DEEM@SIGMOD 2023: 8:1-8:5 - Ties Robroek, Aaron Duane, Ehsan Yousefzadeh-Asl-Miandoab, Pinar Tözün:
Data Management and Visualization for Benchmarking Deep Learning Training Systems. DEEM@SIGMOD 2023: 1:1-1:5 - Clemens Ruck, Maximilian Emanuel Schüle:
Teaching Blue Elephants the Maths for Machine Learning. DEEM@SIGMOD 2023: 2:1-2:4 - Marius Schlegel, Kai-Uwe Sattler:
MLflow2PROV: Extracting Provenance from Machine Learning Experiments. DEEM@SIGMOD 2023: 9:1-9:4 - Haoxiang Zhang, Roque Lopez, Aécio S. R. Santos, Jorge Piazentin Ono, Aline Bessa, Juliana Freire:
Using Pipeline Performance Prediction to Accelerate AutoML Systems. DEEM@SIGMOD 2023: 6:1-6:11 - Cheng Zhen, Amandeep Singh Chabada, Arash Termehchy:
When Can We Ignore Missing Data in Model Training? DEEM@SIGMOD 2023: 4:1-4:4 - Proceedings of the Seventh Workshop on Data Management for End-to-End Machine Learning, DEEM 2023, Seattle, WA, USA, 18 June 2023. ACM 2023 [contents]
- 2022
- Sabri Eyuboglu, Bojan Karlas, Christopher Ré, Ce Zhang, James Zou:
dcbench: a benchmark for data-centric AI systems. DEEM@SIGMOD 2022: 9:1-9:4 - Lampros Flokas, Weiyuan Wu, Jiannan Wang, Nakul Verma, Eugene Wu:
How I stopped worrying about training data bugs and started complaining. DEEM@SIGMOD 2022: 1:1-1:5 - Stefan Grafberger, Paul Groth, Sebastian Schelter:
Towards data-centric what-if analysis for native machine learning pipelines. DEEM@SIGMOD 2022: 3:1-3:5 - Sonia Horchidan, Emmanouil Kritharakis, Vasiliki Kalavri, Paris Carbone:
Evaluating model serving strategies over streaming data. DEEM@SIGMOD 2022: 4:1-4:5 - Shruti Kunde, Sharod Roy Choudhury, Amey Pandit, Rekha Singhal:
Learning-to-learn efficiently with self-learning. DEEM@SIGMOD 2022: 8:1-8:11 - Rui Liu, David Wong, Dave Lange, Patrik Larsson, Vinay Jethava, Qing Zheng:
Accelerating container-based deep learning hyperparameter optimization workloads. DEEM@SIGMOD 2022: 6:1-6:10 - Valerie Restat, Gerrit Boerner, André Conrad, Uta Störl:
GouDa - generation of universal data sets: improving analysis and evaluation of data preparation pipelines. DEEM@SIGMOD 2022: 2:1-2:6 - 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. DEEM@SIGMOD 2022: 5:1-5:4
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