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| 001 | 986802 | ||
| 005 | 20250207124120.0 | ||
| 024 | 7 | _ | |2 ISBN |a 979-8-3503-0788-7 |
| 024 | 7 | _ | |2 ISBN |a 979-8-3503-0789-4 |
| 024 | 7 | _ | |2 ISSN |a 1550-4786 |
| 024 | 7 | _ | |2 ISSN |a 2374-8486 |
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| 024 | 7 | _ | |2 doi |a 10.1109/ICDM58522.2023.00163 |
| 037 | _ | _ | |a RWTH-2024-05390 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Quercia, Alessio |b 0 |e Corresponding author |
| 111 | 2 | _ | |a 2023 IEEE International Conference on Data Mining |c Shanghai |d 2023-12-01 - 2023-12-04 |g ICDM |w Peoples R China |
| 245 | _ | _ | |a SGD Biased towards Early Important Samples for Efficient Training |h online, print |
| 260 | _ | _ | |a Piscataway, NJ |b IEEE |c 2023 |
| 295 | 1 | 0 | |a 23rd IEEE International Conference on Data Mining : ICDM 2023 : proceedings : 1-4 December 2023, Shanghai, China / editors: Guihai Chen, Latifur Khan, Xiaofeng Gao, Meikang Qiu, Witold Pedrycz, Xindong Wu |
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| 700 | 1 | _ | |a Scharr, Hanno |b 2 |
| 700 | 1 | _ | |a Assent, Ira |b 3 |
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