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Analytical theories suggest that higher-quality data can lead to lower test errors in models trained on a fixed data budget.
Thumbs up or thumbs down? semantic orientation applied to unsupervised classification of reviews
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Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
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Message passing adaptive resonance theory for online active semi-supervised learning
Taehyeong Kim, Injune Hwang, Hyundo Lee, Hyunseo Kim, Won-Seok Choi, Joseph J Lim, and Byoung-Tak Zhang · 2021
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Deep learning on a data diet: Finding important examples early in training
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Dataset distillation by matching training trajectories
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