Guaranteeing Reproducibility in Deep Learning Competitions

Brandon Houghton; Stephanie Milani; Nicholay Topin; William Guss; Katja Hofmann; Diego Perez-Liebana; Manuela Veloso; Ruslan Salakhutdinov

arXiv · 2005.06041

Guaranteeing Reproducibility in Deep Learning Competitions

Brandon Houghton·Stephanie Milani·Nicholay Topin·William Guss·Katja Hofmann·Diego Perez-Liebana·Manuela Veloso·Ruslan Salakhutdinov

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Abstract

To encourage the development of methods with reproducible and robust training behavior, we propose a challenge paradigm where competitors are evaluated directly on the performance of their learning procedures rather than pre-trained agents. Since competition organizers re-train proposed methods in a controlled setting they can guarantee reproducibility, and -- by retraining submissions using a held-out test set -- help ensure generalization past the environments on which they were trained.

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Brandon Houghton, Stephanie Milani, Nicholay Topin, William Guss, Katja Hofmann, Diego Perez-Liebana, Manuela Veloso, Ruslan Salakhutdinov. 2020-05-12. Guaranteeing Reproducibility in Deep Learning Competitions. https://arxiv.org/abs/2005.06041

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