Towards Machine Learning with Zero Real-World Data-Reference-Cited by-同舟云学术

Towards Machine Learning with Zero Real-World Data

Published:2019 Issue: Volume: Page:
ISSN:
Container-title:The 5th ACM Workshop on Wearable Systems and Applications - WearSys '19
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Author:

Kang Cholmin¹,Jung Hyunwoo²,Lee Youngki²

Affiliation:

1. SungKyunKwan University, Seoul, South Korea

2. Seoul National University, Seoul, South Korea

Funder

Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government(MSIT)

Publisher

ACM Press

Reference13 articles.

1. Arthur Le Guennec, Simon Malinowski, and Romain Tavenard. 2016. Data Augmentation for Time Series Classification using Convolutional Neural Networks. In ECML/PKDD Workshop on Advanced Analytics and Learning on Temporal Data. Riva Del Garda, Italy. https://halshs.archives-ouvertes.fr/halshs-01357973

2. Sergey Levine and Pieter Abbeel. 2014. Learning Neural Network Policies with Guided Policy Search under Unknown Dynamics. In Advances in Neural Information Processing Systems 27,, Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 1071--1079. http://papers.nips.cc/paper/5444-learning-neural-network-policies-with-guided-policy-search-under-unknown-dynamics.pdf

3. X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel. 2018. Sim-to-Real Transfer of Robotic Control with Dynamics Randomization. In 2018 IEEE International Conference on Robotics and Automation (ICRA). 1--8. https://doi.org/10.1109/ICRA.2018.8460528

4. Attila Reiss. 2012. PAMAP2 Physical Activity Monitoring Data Set. http://archive.ics.uci.edu/ml/datasets/pamap2

5. physical

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