A comparative study of three deep learning models for PM2.5 interpolation-Reference-Cited by-同舟云学术

A comparative study of three deep learning models for PM2.5 interpolation

Published:2022-08-22 Issue: Volume: Page:
ISSN:
Container-title:International Database Engineered Applications Symposium
language:
Short-container-title:

Author:

Li Lixin¹

Affiliation:

1. Department of Computer Science, Georgia Southern University, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3548785.3548809

Reference27 articles.

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3. Preeti Bamane and Mangal Patil . 2020. INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY A comparative study of different LSTM neural networks in predicting air pollutant concentrations. Indian Journal of Science and Technology 13 (11 2020 ), 3664. https://doi.org/10.17485/IJST/v13i35.1276 Preeti Bamane and Mangal Patil. 2020. INDIAN JOURNAL OF SCIENCE AND TECHNOLOGY A comparative study of different LSTM neural networks in predicting air pollutant concentrations. Indian Journal of Science and Technology 13 (11 2020), 3664. https://doi.org/10.17485/IJST/v13i35.1276

4. Yitian Chen , K Yanfei , C Yixiong , and W Zizhuo . 2020. Probabilistic Forecasting with Temporal Convolutional Neural Network. Neurocomputing 399 (03 2020 ). https://doi.org/10.1016/j.neucom.2020.03.011 Yitian Chen, K Yanfei, C Yixiong, and W Zizhuo. 2020. Probabilistic Forecasting with Temporal Convolutional Neural Network. Neurocomputing 399 (03 2020). https://doi.org/10.1016/j.neucom.2020.03.011

5. Aditya Devarakonda Maxim Naumov and Michael Garland. 2018. AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks. arxiv:1712.02029 [cs.LG] Aditya Devarakonda Maxim Naumov and Michael Garland. 2018. AdaBatch: Adaptive Batch Sizes for Training Deep Neural Networks. arxiv:1712.02029 [cs.LG]