Balancing Familiarity and Curiosity in Data Exploration with Deep Reinforcement Learning-Reference-Cited by-同舟云学术

Balancing Familiarity and Curiosity in Data Exploration with Deep Reinforcement Learning

Published:2021-06-20 Issue: Volume: Page:
ISSN:
Container-title:Fourth Workshop in Exploiting AI Techniques for Data Management
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Author:

Personnaz Aurélien¹,Amer-Yahia Sihem¹,Berti-Equille Laure²,Fabricius Maximilian³,Subramanian Srividya³

Affiliation:

1. CNRS, Univ. Grenoble Alpes, France

2. IRD, ESPACE-DEV, Montpellier, France

3. Max Planck Institute for Extraterrestrial Physics, Germany

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3464509.3464884

Reference32 articles.

1. S. Amer-Yahia S. Kleisarchaki N. K. Kolloju L. V. Lakshmanan and R. H. Zamar. 2017. Exploring Rated Datasets with Rating Maps. In WWW. S. Amer-Yahia S. Kleisarchaki N. K. Kolloju L. V. Lakshmanan and R. H. Zamar. 2017. Exploring Rated Datasets with Rating Maps. In WWW.

2. O. Bar El T. Milo and A. Somech. 2020. Automatically generating data exploration sessions using deep reinforcement learning. In SIGMOD. 1527–1537. O. Bar El T. Milo and A. Somech. 2020. Automatically generating data exploration sessions using deep reinforcement learning. In SIGMOD. 1527–1537.

3. L. Biewald. 2020. Experiment Tracking with Weights and Biases. https://www.wandb.com/ Software available from wandb.com. L. Biewald. 2020. Experiment Tracking with Weights and Biases. https://www.wandb.com/ Software available from wandb.com.

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