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The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning

Denevi, G; Pontil, M; Ciliberto, C; (2020) The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning. In: Proceedings of NeurIPS 2020: Thirty-fourth Conference on Neural Information Processing Systems. Neural Information Processing Systems: Virtual conference. (In press).

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NeurIPS-2020-the-advantage-of-conditional-meta-learning-for-biased-regularization-and-fine-tuning-Paper.pdf - Published version
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Type: Proceedings paper
Title: The Advantage of Conditional Meta-Learning for Biased Regularization and Fine-Tuning
Event: NeurIPS 2020: Thirty-fourth Conference on Neural Information Processing Systems
Publisher version: https://nips.cc/Conferences/2020
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10115253
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