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Found 7 papers, 3 papers with code
Kubric: A scalable dataset generator
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.
Quantifying Long Range Dependence in Language and User Behavior to improve RNNs
Characterizing temporal dependence patterns is a critical step in understanding the statistical properties of sequential data.
Scaling Up Collaborative Filtering Data Sets through Randomized Fractal Expansions
Recommender system research suffers from a disconnect between the size of academic data sets and the scale of industrial production systems.
Towards Neural Mixture Recommender for Long Range Dependent User Sequences
Our approach employs a mixture of models, each with a different temporal range.
Scalable Realistic Recommendation Datasets through Fractal Expansions
A larger version features 655 billion ratings, 7 million items and 17 million users.
Top-K Off-Policy Correction for a REINFORCE Recommender System
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.
Expert Level control of Ramp Metering based on Multi-task Deep Reinforcement Learning
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.
