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2020ccf-bdci-spatial-temporal-road-condition-prediction

alinet

Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation, AAAI 2020

austin-weather-prediction-using-lstm

awesome-graph-classification

A collection of important graph embedding, classification and representation learning papers with implementations.

awesome-trustworthy-deep-learning

A curated list of trustworthy deep learning papers. Daily updating...

awsome-multi-modal-based-phm

Awsome-Multi-modal-based PHM （基于多模态的故障诊断和预测，持续更新）

bayesian-neural-networks

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

bidirectional-lstm-and-convolutional-neural-network-for-temperature-prediction

We use weather data of Ulsan, Korea from 1980 to 2017 to predict temperature.

causalpinns

chinese-annotator

Annotator for Chinese Text Corpus (UNDER DEVELOPMENT) 中文文本标注工具

chinese-bert-wwm

Pre-Training with Whole Word Masking for Chinese BERT（中文BERT-wwm系列模型）

chinese-text-classification-pytorch

中文文本分类，TextCNN，TextRNN，FastText，TextRCNN，BiLSTM_Attention，DPCNN，Transformer，基于pytorch，开箱即用。

clinicalbert

repository for Publicly Available Clinical BERT Embeddings

clustered_ccrf_model

The implementation of proposed model in paper Yi, F., Yu, Z., et al. An Integrated Model for Crime Prediction Using Temporal and Spatial Factors. In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 1386-1391). IEEE.

code-of-csdn

csdn上面的一些相关代码

cogdl

CogDL: An Extensive Research Toolkit for Graphs

cst-ml

The codes and data of paper "cST-ML: Continuous Spatial-Temporal Meta-Learning for Traffic Dynamics Prediction"

ctle

Pre-training Context and Time Aware Location Embeddings from Spatial-Temporal Trajectories for User Next Location Prediction

curve-gcn

Official PyTorch code for Curve-GCN (CVPR 2019)

deep-learning-for-autonomous-driving

The purpose of this project is the development of an End-to-End learning model in order to predict the steering angle of an autonomous car. The proposed method uses monocular vision in order to acomplish the prediction task. Specifically, a CNN followed by LSTM units, is trained in order to manage both spatial and temporal information of the image sequence. In addition, a fusion with a second CNN that uses past prediction as inputs, is proposed, in order to improve the temporal information available. Both of the architectures were trained and tested on human driving data, provided by Udacity Challenge 2.

deep_learning_weather_forecasting

Deep Learning for Weather Forecasting, accepted applied data science of KDD 2019

deepke

基于深度学习的开源中文关系抽取框架

deepstn

Codes for AAAI 2019 DeepSTN+: Context-aware Spatial-Temporal Neural Network for Crowd Flow Prediction in Metropolis

dg-phm

This is a reposotory that includes paper、code and datasets about domain generalization-based fault diagnosis and prognosis. (基于领域泛化的故障诊断和预测，持续更新)

digital-twin-approach-for-damage-tolerant-mission-planning-under-uncertainty

The digital twin paradigm that integrates the information obtained from sensor data, physics models, as well as operational and inspection/maintenance/repair history of a system (or a component) of interest, can potentially be used to optimize operational parameters of the system in order to achieve a desired performance or reliability goal. In this article, we develop a methodology for intelligent mission planning using the digital twin approach, with the objective of performing the required work while meeting the damage tolerance requirement. The proposed approach has three components: damage diagnosis, damage prognosis, and mission optimization. All three components are affected by uncertainty regarding system properties, operational parameters, loading and environment, as well as uncertainties in sensor data and prediction models. Therefore the proposed methodology includes the quantification of the uncertainty in diagnosis, prognosis, and optimization, considering both aleatory and epistemic uncertainty sources. We discuss an illustrative fatigue crack growth experiment to demonstrate the methodology for a simple mechanical component, and build a digital twin for the component. Using a laboratory experiment that utilizes the digital twin, we show how the trio of probabilistic diagnosis, prognosis, and mission planning can be used in conjunction with the digital twin of the component of interest to optimize the crack growth over single or multiple missions of fatigue loading, thus optimizing the interval between successive inspection, maintenance, and repair actions.

dlwp

Deep Learning Weather Prediction

dlwp-cs

Deep learning models for global weather prediction on a cubed sphere

domain-generalization-fault-diagnosis-benchmark

This is a benckmark for domain generalization-based fault diagnosis （基于领域泛化的相关代码）

duronet-for-crime-prediction

The source code of the paper "DuroNet: A Dual-Robust Enhanced Spatial-Temporal Learning Network for Urban Crime Prediction"

gat

Graph Attention Networks (https://arxiv.org/abs/1710.10903)