Multiple CNN Variants and Ensemble Learning for Sunspot Group Classification by Magnetic Type
Rongxin Tang,Xunwen Zeng,Zhou Chen,Wenti Liao,Jing-Song Wang,Bingxian Luo,Y. P. Chen,Yanmei Cui,Meng Zhou,Xiaohua Deng,Haimeng Li,Kai Yuan,Sheng Hong,Zhiping Wu +13 more
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This article is published in Astrophysical Journal Supplement Series.The article was published on 2021-12-01 and is currently open access. It has received 8 citations till now. The article focuses on the topics: Ensemble learning.read more
Citations
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Prediction of Global Ionospheric TEC Based on Deep Learning
TL;DR: In this article , a prediction model of global IGS-TEC maps is established based on testing several different LSTM network (LSTM)-based algorithms to explore a direction that can effectively alleviate the increasing error with prediction time.
Journal ArticleDOI
The Prediction of Storm‐Time Thermospheric Mass Density by LSTM‐Based Ensemble Learning
TL;DR: In this article , an available prediction model is established by Long Short-Term Memory (LSTM)-based ensemble learning algorithms, which provides a promising way to give reliable and stable predictions of thermospheric mass density.
Journal ArticleDOI
The Short‐Time Prediction of the Energetic Electron Flux in the Planetary Radiation Belt Based on Stacking Ensemble‐Learning Algorithm
TL;DR: In this article , the authors proposed a new ensemble machine leaning model for differential electron flux from 30 keV to 4 MeV in the Earth's radiation belts based on the RBSP-A observation data from March 2013 to December 2017.
Journal ArticleDOI
Application of Deep Reinforcement Learning to Major Solar Flare Forecasting
TL;DR: In this paper , the authors applied deep Q-network and double DQN to predict major solar flares with good skill scores, such as HSS, F1, TSS, and ApSS.
Journal ArticleDOI
Predicting CME arrival time through data integration and ensemble learning
Khalid Abdulrahman Alobaid,Yasser Abduallah,Jason T. L. Wang,Haimin Wang,Haodi Jiang,Yan Xu,Vasyl Yurchyshyn,Hongyang Zhang,Huseyin Cavus,Ju Jing +9 more
TL;DR: This study collects and integrates eruptive events from two solar cycles, #23 and #24, from 1996 to 2021, with a total of 363 geoeffective CMEs, and proposes an ensemble learning approach, named CMETNet, for predicting the arrival time of Cmes from the Sun to the Earth.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.
Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
Proceedings ArticleDOI
Xception: Deep Learning with Depthwise Separable Convolutions
TL;DR: This work proposes a novel deep convolutional neural network architecture inspired by Inception, where Inception modules have been replaced with depthwise separable convolutions, and shows that this architecture, dubbed Xception, slightly outperforms Inception V3 on the ImageNet dataset, and significantly outperforms it on a larger image classification dataset.