{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T06:26:55Z","timestamp":1778048815972,"version":"3.51.4"},"reference-count":16,"publisher":"Association for Computing Machinery (ACM)","issue":"12","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2021,7]]},"abstract":"<jats:p>\n            Deep learning (DL) has widespread applications and has revolutionized many industries. Although automated machine learning (AutoML) can help us away from coding for DL models, the acquisition of\n            <jats:italic>lots of<\/jats:italic>\n            high-quality data for model training remains a main bottleneck for many DL projects, simply because it requires high human cost. Despite many works on weak supervision (\n            <jats:italic>i.e.<\/jats:italic>\n            , adding weak labels to seen data) and data augmentation (\n            <jats:italic>i.e.<\/jats:italic>\n            , generating more data based on seen data), automatically acquiring training data, via smartly searching a pool of training data collected from open ML benchmarks and data markets, is not explored.\n          <\/jats:p>\n          <jats:p>In this demonstration, we demonstrate a new system, automatic data acquisition (AutoData), which automatically searches training data from a heterogeneous data repository and interacts with AutoML. It faces two main challenges. (1) How to search high-quality data from a large repository for a given DL task? (2) How does AutoData interact with AutoML to guide the search? To address these challenges, we propose a reinforcement learning (RL)-based framework in AutoData to guide the iterative search process. AutoData encodes current training data and feedbacks of AutoML, learns a policy to search fresh data, and trains in iterations. We demonstrate with two real-life scenarios, image classification and relational data prediction, showing that AutoData can select high-quality data to improve the model.<\/jats:p>","DOI":"10.14778\/3476311.3476333","type":"journal-article","created":{"date-parts":[[2021,10,28]],"date-time":"2021-10-28T22:48:56Z","timestamp":1635461336000},"page":"2739-2742","source":"Crossref","is-referenced-by-count":19,"title":["Automatic data acquisition for deep learning"],"prefix":"10.14778","volume":"14","author":[{"given":"Jiabin","family":"Liu","sequence":"first","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fu","family":"Zhu","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengliang","family":"Chai","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuyu","family":"Luo","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nan","family":"Tang","sequence":"additional","affiliation":[{"name":"QCRI, Qatar"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,10,28]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3318464.3389772"},{"key":"e_1_2_1_2_1","volume-title":"Incentive-Based Entity Collection Using Crowdsourcing. 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DSE (2020).","DOI":"10.1007\/s41019-019-00115-y"},{"key":"e_1_2_1_9_1","volume-title":"ICML","author":"Koh Pang Wei","year":"2017"},{"key":"e_1_2_1_10_1","unstructured":"M. Li H. Wang and J. Li. 2020. Mining Conditional Functional Dependency Rules on Big Data. Big Data Mining and Analytics 03 01 Article 68 (2020) 16 pages.  M. Li H. Wang and J. Li. 2020. Mining Conditional Functional Dependency Rules on Big Data. Big Data Mining and Analytics 03 01 Article 68 (2020) 16 pages."},{"key":"e_1_2_1_11_1","doi-asserted-by":"crossref","unstructured":"W. Li Y. Zhang Y. Sun W. Wang M. Li W. Zhang and X. Lin. 2020. Approximate Nearest Neighbor Search on High Dimensional Data - Experiments Analyses and Improvement. IEEE Trans. Knowl. Data Eng. (2020).  W. Li Y. Zhang Y. Sun W. Wang M. Li W. Zhang and X. Lin. 2020. Approximate Nearest Neighbor Search on High Dimensional Data - Experiments Analyses and Improvement. IEEE Trans. Knowl. Data Eng . 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