1 code implementation • CVPR 2022 • Klaus Greff, Francois Belletti, Lucas Beyer, Carl Doersch, Yilun Du, Daniel Duckworth, David J. Fleet, Dan Gnanapragasam, Florian Golemo, Charles Herrmann, Thomas Kipf, Abhijit Kundu, Dmitry Lagun, Issam Laradji, Hsueh-Ti, Liu, Henning Meyer, Yishu Miao, Derek Nowrouzezahrai, Cengiz Oztireli, Etienne Pot, Noha Radwan, Daniel Rebain, Sara Sabour, Mehdi S. M. Sajjadi, Matan Sela, Vincent Sitzmann, Austin Stone, Deqing Sun, Suhani Vora, Ziyu Wang, Tianhao Wu, Kwang Moo Yi, Fangcheng Zhong, Andrea Tagliasacchi
Data is the driving force of machine learning, with the amount and quality of training data often being more important for the performance of a system than architecture and training details.
no code implementations • 23 May 2019 • Francois Belletti, Minmin Chen, Ed H. Chi
Characterizing temporal dependence patterns is a critical step in understanding the statistical properties of sequential data.
no code implementations • 8 Apr 2019 • Francois Belletti, Karthik Lakshmanan, Walid Krichene, Nicolas Mayoraz, Yi-fan Chen, John Anderson, Taylor Robie, Tayo Oguntebi, Dan Shirron, Amit Bleiwess
Recommender system research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems.
no code implementations • 22 Feb 2019 • Jiaxi Tang, Francois Belletti, Sagar Jain, Minmin Chen, Alex Beutel, Can Xu, Ed H. Chi
Our approach employs a mixture of models, each with a different temporal range.
1 code implementation • 23 Jan 2019 • Francois Belletti, Karthik Lakshmanan, Walid Krichene, Yi-fan Chen, John Anderson
A larger version features 655 billion ratings, 7 million items and 17 million users.
1 code implementation • 6 Dec 2018 • Minmin Chen, Alex Beutel, Paul Covington, Sagar Jain, Francois Belletti, Ed Chi
The contributions of the paper are: (1) scaling REINFORCE to a production recommender system with an action space on the orders of millions; (2) applying off-policy correction to address data biases in learning from logged feedback collected from multiple behavior policies; (3) proposing a novel top-K off-policy correction to account for our policy recommending multiple items at a time; (4) showcasing the value of exploration.
no code implementations • 30 Jan 2017 • Francois Belletti, Daniel Haziza, Gabriel Gomes, Alexandre M. Bayen
This article shows how the recent breakthroughs in Reinforcement Learning (RL) that have enabled robots to learn to play arcade video games, walk or assemble colored bricks, can be used to perform other tasks that are currently at the core of engineering cyberphysical systems.