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First session for the competition program at NeurIPS2020.
Machine learning competitions have grown in popularity and impact over the last decade, emerging as an effective means to advance the state of the art by posing well-structured, relevant, and challenging problems to the community at large. Motivated by a reward or merely the satisfaction of seeing their machine learning algorithm reach the top of a leaderboard, practitioners innovate, improve, and tune their approach before evaluating on a held-out dataset or environment. The competition track of NeurIPS has matured in 2020, its fourth year, with a considerable increase in both the number of challenges and the diversity of domains and topics. A total of 16 competitions are featured this year as part of the track, with 8 competitions associated to each of the two days. The list of competitions that ar part of the program are available here:
https://neurips.cc/Conferences/2020/CompetitionTrack
Fri 8:00 a.m. - 8:15 a.m.
|
Opening - Competition Track Session
(
Intro
)
SlidesLive Video » |
Katja Hofmann · Hugo Jair Escalante 🔗 |
Fri 8:15 a.m. - 8:35 a.m.
|
3D+texture garment reconstruction challenge design (data, metrics, tracks, etc)
(
Live oral presentation
)
|
Hugo Bertiche 🔗 |
Fri 8:35 a.m. - 9:00 a.m.
|
3D+texture garment reconstruction challenge results
(
Live oral presentation
)
|
Meysam Madadi 🔗 |
Fri 9:00 a.m. - 9:03 a.m.
|
Opening the L2RPN challenge @ NeurIPS2020
(
Intro
)
|
Antoine Marot 🔗 |
Fri 9:03 a.m. - 9:13 a.m.
|
Winning the L2RPN challenge
(
Oral presentation
)
SlidesLive Video » |
Antoine Marot 🔗 |
Fri 9:13 a.m. - 9:23 a.m.
|
A L2RPN Winning approach
(
Oral presentation
)
SlidesLive Video » |
Jixiang LU 🔗 |
Fri 9:23 a.m. - 9:33 a.m.
|
The Best L2RPN wining approach
(
Oral presentation
)
SlidesLive Video » |
Bo Zhou 🔗 |
Fri 9:33 a.m. - 9:40 a.m.
|
L2RPN Post Challenge open questions
(
Oral presentation
)
SlidesLive Video » |
Antoine Marot 🔗 |
Fri 9:40 a.m. - 9:45 a.m.
|
Closing and ceremony award
(
Outro
)
|
Antoine Marot 🔗 |
Fri 10:00 a.m. - 10:03 a.m.
|
Introducing the Hide-and-Seek privacy challenge
(
Intro
)
|
James Jordon 🔗 |
Fri 10:03 a.m. - 10:18 a.m.
|
The importance of synthetic data
(
Oral presentation
)
SlidesLive Video » |
James Jordon 🔗 |
Fri 10:18 a.m. - 10:28 a.m.
|
Synthetic data in the healthcare setting
(
Oral presentation
)
SlidesLive Video » |
James Jordon 🔗 |
Fri 10:28 a.m. - 10:38 a.m.
|
What we learned from the Hide-and-Seek privacy challenge
(
Oral presentation
)
|
James Jordon 🔗 |
Fri 10:38 a.m. - 10:43 a.m.
|
Closing remarks
(
Outro
)
|
James Jordon 🔗 |
Fri 11:00 a.m. - 11:16 a.m.
|
Background on black box optimization (BBO)
(
Oral presentation
)
SlidesLive Video » |
Ryan Turner 🔗 |
Fri 11:16 a.m. - 11:27 a.m.
|
BBO challenge platform with Valohai
(
Oral presentation
)
SlidesLive Video » |
Juha Kiili 🔗 |
Fri 11:27 a.m. - 11:32 a.m.
|
Spotlight for 1st place (BBO challenge)
(
Oral presentation
)
SlidesLive Video » |
Ryan Turner · Alexander Cowen-Rivers 🔗 |
Fri 11:32 a.m. - 11:37 a.m.
|
Spotlight for 2nd place (BBO challenge)
(
Oral presentation
)
|
Ryan Turner · Jiwei Liu 🔗 |
Fri 11:37 a.m. - 11:42 a.m.
|
Spotlight for 3rd place (BBO challenge)
(
Oral presentation
)
SlidesLive Video » |
Ryan Turner · Mikita Sazanovich 🔗 |
Fri 2:00 p.m. - 2:07 p.m.
|
Opening the SpaceNet 7 Challenge @ NeurIPS2020
(
Oral presentation
)
|
Adam Van Etten 🔗 |
Fri 2:07 p.m. - 2:15 p.m.
|
Introduction to SpaceNet
(
Oral presentation
)
|
Jacob Shermeyer 🔗 |
Fri 2:15 p.m. - 2:25 p.m.
|
The SpaceNet 7 Dataset
(
Oral presentation
)
|
Jesus Martinez-Manso 🔗 |
Fri 2:25 p.m. - 2:30 p.m.
|
The SpaceNet 7 Metric
(
Oral presentation
)
|
Adam Van Etten 🔗 |
Fri 2:30 p.m. - 2:40 p.m.
|
The Winners of SpaceNet 7
(
Oral presentation
)
|
Adam Van Etten 🔗 |
Fri 2:40 p.m. - 2:45 p.m.
|
SpaceNet 7 Closing and Future Plans
(
Oral presentation
)
|
Adam Van Etten 🔗 |
Fri 3:00 p.m. - 3:05 p.m.
|
introduction to the 2020 NeurIPS education challenge
(
Oral presentation
)
SlidesLive Video » |
Angus Lamb 🔗 |
Fri 3:05 p.m. - 3:20 p.m.
|
Competition overview: motivation, impact, dataset, tasks
(
Oral presentation
)
SlidesLive Video » |
Angus Lamb 🔗 |
Fri 3:20 p.m. - 3:30 p.m.
|
Competition results and insights
(
Oral presentation
)
SlidesLive Video » |
Jack Wang 🔗 |
Fri 3:30 p.m. - 3:35 p.m.
|
Beyond the competition: what's next?
(
Oral presentation
)
SlidesLive Video » |
Jack Wang 🔗 |
Fri 3:35 p.m. - 3:45 p.m.
|
Q&A and discussion
(
Outro
)
|
Jack Wang · Angus Lamb 🔗 |
Fri 4:00 p.m. - 4:05 p.m.
|
Traffic Map Movies - An Introduction to the Traffic4cast Challenge
(
Oral presentation
)
SlidesLive Video » |
Sepp Hochreiter 🔗 |
Fri 4:05 p.m. - 4:10 p.m.
|
The Traffic4cast Competition Design and Data
(
Oral presentation
)
SlidesLive Video » |
Michael Kopp 🔗 |
Fri 4:10 p.m. - 4:12 p.m.
|
The Best Traffic4Cast Submissions
(
Intro
)
|
D Kreil 🔗 |
Fri 4:12 p.m. - 4:17 p.m.
|
1st prize: Utilizing UNet for the Future Traffic Map Prediction - Traffic4cast highlight talk
(
Oral presentation
)
SlidesLive Video » |
Sungbin Choi 🔗 |
Fri 4:17 p.m. - 4:22 p.m.
|
2nd prize: TLab: Traffic Map Movie Forecasting Based on HR-NET - Traffic4cast highlight talk
(
Oral presentation
)
SlidesLive Video » |
Fanyou Wu 🔗 |
Fri 4:22 p.m. - 4:27 p.m.
|
3rd prize: Towards Good Practices of U-Net for Traffic Forecasting - Traffic4cast highlight talk
(
Oral presentation
)
SlidesLive Video » |
Jingwei Xu 🔗 |
Fri 4:27 p.m. - 4:32 p.m.
|
Graph Ensemble Net and the Importance of Feature & Loss Function Design for Traffic Prediction - Traffic4cast highlight talk
(
Oral presentation
)
SlidesLive Video » |
Qi Qi 🔗 |
Fri 4:32 p.m. - 4:37 p.m.
|
Uncertainty Intervals for Graph-based Spatio-Temporal Traffic Prediction - Traffic4cast highlight talk
(
Oral presentation
)
SlidesLive Video » |
Tijs Maas 🔗 |
Fri 4:37 p.m. - 4:45 p.m.
|
Traffic4cast Award Ceremony, Outlook, and Follow Up Challenges
(
Discussion panel
)
|
D Kreil 🔗 |
Fri 5:00 p.m. - 5:15 p.m.
|
The Hateful Memes Challenge: Competition Overview
(
Oral presentation
)
SlidesLive Video » |
Douwe Kiela 🔗 |
Fri 5:15 p.m. - 5:45 p.m.
|
The Hateful Memes Challenge: Live award ceremony and winner presentations
(
Discussion panel
)
|
Douwe Kiela 🔗 |
Author Information
Hugo Jair Escalante (INAOE)
Katja Hofmann (Microsoft Research)
Dr. Katja Hofmann is a Principal Researcher at the [Game Intelligence](http://aka.ms/gameintelligence/) group at [Microsoft Research Cambridge, UK](https://www.microsoft.com/en-us/research/lab/microsoft-research-cambridge/). There, she leads a research team that focuses on reinforcement learning with applications in modern video games. She and her team strongly believe that modern video games will drive a transformation of how we interact with AI technology. One of the projects developed by her team is [Project Malmo](https://www.microsoft.com/en-us/research/project/project-malmo/), which uses the popular game Minecraft as an experimentation platform for developing intelligent technology. Katja's long-term goal is to develop AI systems that learn to collaborate with people, to empower their users and help solve complex real-world problems. Before joining Microsoft Research, Katja completed her PhD in Computer Science as part of the [ILPS](https://ilps.science.uva.nl/) group at the [University of Amsterdam](https://www.uva.nl/en). She worked with Maarten de Rijke and Shimon Whiteson on interactive machine learning algorithms for search engines.
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