Modeling Marked Temporal Point Process Using Multi-relation Structure RNN

Cai, Hongyun; Nguyen, Thanh Tung; Li, Yan; Zheng, Vincent W.; Chen, Binbin; Cong, Gao; Li, Xiaoli

doi:10.1007/s12559-019-09690-8

Cited by 4 publications

(3 citation statements)

References 31 publications

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“…Where O i is the output of the model after ith iteration, S i is the input parameters, W i is the weight of the input layer, and W r is the weights of the recurrent layer. RNN uses BP to optimize the network [41].…”

Section: ) Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

et al. 2020

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Predicting the time, location and magnitude of an earthquake is a challenging job as an earthquake does not show specific patterns resulting in inaccurate predictions. Techniques based on Artificial Intelligence (AI) are well known for their capability to find hidden patterns in data. In the case of earthquake prediction, these models also produce a promising outcome. This work systematically explores the contributions made to date in earthquake prediction using AI-based techniques. A total of 84 scientific research papers, which reported the use of AI-based techniques in earthquake prediction, have been selected from different academic databases. These studies include a range of AI techniques including rule-based methods, shallow machine learning and deep learning algorithms. Covering all existing AI-based techniques in earthquake prediction, this paper provides an account of the available methodologies and a comparative analysis of their performances. The performance comparison has been reported from the perspective of used datasets and evaluation metrics. Furthermore, using comparative analysis of performances the paper aims to facilitate the selection of appropriate techniques for earthquake prediction. Towards the end, it outlines some open challenges and potential research directions in the field.

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Section: ) Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

et al. 2020

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“…Most of the existing research works are based on these eight seismic indicators [22]. They also showed a performance comparison between radial basis function neural network (RBFNN), BPNN and recurrent neural network (RNN), where RNN showed the best detection probability [40]. Chen et al [41] adopted a memorized knowledge approach for image captioning using RNN.…”

Section: Introductionmentioning

confidence: 99%

Attention-Based Bi-Directional Long-Short Term Memory Network for Earthquake Prediction

et al. 2021

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An earthquake is a tremor felt on the surface of the earth created by the movement of the major pieces of its outer shell. Till now, many attempts have been made to forecast earthquakes, which saw some success, but these attempted models are specific to a region. In this paper, an earthquake occurrence and location prediction model is proposed. After reviewing the literature, long short-term memory (LSTM) is found to be a good option for building the model because of its memory-keeping ability. Using the Keras tuner, the best model was selected from candidate models, which are composed of combinations of various LSTM architectures and dense layers. This selected model used seismic indicators from the earthquake catalog of Bangladesh as features to predict earthquakes of the following month. Attention mechanism was added to the LSTM architecture to improve the model's earthquake occurrence prediction accuracy, which was 74.67%. Additionally, a regression model was built using LSTM and dense layers to predict the earthquake epicenter as a distance from a predefined location, which provided a root mean square error of 1.25.

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“…[31] estimated traffic time from trajectory of taxi in different fine-grained time intervals based on deep learning. [32] designed a RNN model with a multi-relational structure, which not only captures the traditional time dependence, but also captures the explicit multi-relational topological dependence through a hierarchical attention mechanism.…”

Section: Introductionmentioning

confidence: 99%

Information Cascades Prediction With Graph Attention

et al. 2021

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The cascades prediction aims to predict the possible information diffusion path in the future based on cascades of the social network. Recently, the existing researches based on deep learning have achieved remarkable results, which indicates the great potential to support cascade prediction task. However, most prior arts only considered either cascade features or user relationship network to predict cascade, which leads to the performance limitation because of the lack of unified modeling for the potential relationship between them. To that end, in this paper, we propose a recurrent neural network model with graph attention mechanism, which constructs a seq2seq framework to learn the spatial-temporal cascade features. Specifically, for user spatial feature, we learn potential relationship among users based on social network through graph attention network. Then, for temporal feature, a recurrent neural network is built to learn their structural context in several different time intervals based on timestamp with a time-decay attention. Finally, we predict the next user with the latest cascade representation which obtained by above method. Experimental results on two real-world datasets show that our model achieves better performance than the baselines on the both evaluation metrics of HITS and mean average precision.

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Modeling Marked Temporal Point Process Using Multi-relation Structure RNN

Cited by 4 publications

References 31 publications

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

Application of Artificial Intelligence in Predicting Earthquakes: State-of-the-Art and Future Challenges

Attention-Based Bi-Directional Long-Short Term Memory Network for Earthquake Prediction

Information Cascades Prediction With Graph Attention

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