Search Results for author: Jeremiah Harmsen

Found 4 papers, 3 papers with code

RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement Learning

1 code implementation4 Nov 2021 Sabela Ramos, Sertan Girgin, Léonard Hussenot, Damien Vincent, Hanna Yakubovich, Daniel Toyama, Anita Gergely, Piotr Stanczyk, Raphael Marinier, Jeremiah Harmsen, Olivier Pietquin, Nikola Momchev

We introduce RLDS (Reinforcement Learning Datasets), an ecosystem for recording, replaying, manipulating, annotating and sharing data in the context of Sequential Decision Making (SDM) including Reinforcement Learning (RL), Learning from Demonstrations, Offline RL or Imitation Learning.

Decision Making Imitation Learning +2

TensorFlow-Serving: Flexible, High-Performance ML Serving

no code implementations17 Dec 2017 Christopher Olston, Noah Fiedel, Kiril Gorovoy, Jeremiah Harmsen, Li Lao, Fangwei Li, Vinu Rajashekhar, Sukriti Ramesh, Jordan Soyke

We describe TensorFlow-Serving, a system to serve machine learning models inside Google which is also available in the cloud and via open-source.

BIG-bench Machine Learning

Wide & Deep Learning for Recommender Systems

31 code implementations24 Jun 2016 Heng-Tze Cheng, Levent Koc, Jeremiah Harmsen, Tal Shaked, Tushar Chandra, Hrishi Aradhye, Glen Anderson, Greg Corrado, Wei Chai, Mustafa Ispir, Rohan Anil, Zakaria Haque, Lichan Hong, Vihan Jain, Xiaobing Liu, Hemal Shah

Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.

Click-Through Rate Prediction Feature Engineering +2

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