Guaranteeing Reproducibility in Deep Learning Competitions

Guaranteeing Reproducibility in Deep Learning Competitions

12 May 2020

Brandon Houghton

Stephanie Milani

William H. Guss

Diego Perez-Liebana

Ruslan Salakhutdinov

Papers citing "Guaranteeing Reproducibility in Deep Learning Competitions"

5 / 5 papers shown

Title
Emotion-Cause Pair Extraction as Question Answering Huu-Hiep Nguyen Minh-Tien Nguyen 48 5 0 05 Jan 2023
MineRL Diamond 2021 Competition: Overview, Results, and Lessons Learned Anssi Kanervisto Stephanie Milani Karolis Ramanauskas Nicholay Topin Zichuan Lin ... Franccois Fleuret Alexander Nikulin Yury Belousov Oleg Svidchenko A. Shpilman OffRL 57 31 0 17 Feb 2022
Towards robust and domain agnostic reinforcement learning competitions William H. Guss Stephanie Milani Nicholay Topin Brandon Houghton Sharada Mohanty ... Lu Liu Daichi Nishio Toi Tsuneda Karolis Ramanauskas Gabija Juceviciute OOD 19 2 0 07 Jun 2021
The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors William H. Guss Cayden R. Codel Katja Hofmann Brandon Houghton Noburu Kuno ... John Schulman Manuela Veloso Nicholay Topin Avinash Ummadisingu Phillip Wang OffRL 17 63 0 26 Jan 2021
Retrospective Analysis of the 2019 MineRL Competition on Sample Efficient Reinforcement Learning Stephanie Milani Nicholay Topin Brandon Houghton William H. Guss Sharada Mohanty Keisuke Nakata Oriol Vinyals N. Kuno OffRL 33 27 0 10 Mar 2020