This repository presents the AK-MCS algorithm with a Hierarchical Gaussian Processes on some benchmark problems alongside baseline methods.

高斯过程回归

Code of NIPS18 Paper: BRITS: Bidirectional Recurrent Imputation for Time Series

Official Implementation of "GRPE: Relative Positional Encoding for Graph Transformer"

Granger causality discovery for neural networks.

Multi-directional Recurrent Neural Networks (MRNN) - IEEE TBME 2019

TensorFlow implementation for the GP-VAE model described in https://arxiv.org/abs/1907.04155

基于Qt的托盘天气预报

Weather 是使用 C++ & Qt Quick 开发的一款天气 App，理论上可以在 Windows、Mac OS、Linux、Android、iOS 等平台上运行。

LibCity: An Open Library for Urban Spatial-temporal Data Mining

Graph Neural Network

Implementation of Diffusion Convolutional Recurrent Neural Network in Tensorflow

Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at

few-shot models for short-term traffic prediction

DocEnTr: An end-to-end document image enhancement transformer - ICPR 2022

Multivariate imputation and matrix completion algorithms implemented in Python

Source code for EMNLP 2020 paper: Double Graph Based Reasoning for Document-level Relation Extraction

Official repository for the paper "Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations" (NeurIPS 2022)

Spatio-Temporal Graph Convolutional Networks

Complex seismic data reconstruction and interpolation

The official PyTorch implementation of the paper "SAITS: Self-Attention-based Imputation for Time Series". A fast and state-of-the-art (SOTA) deep-learning neural network model for efficient time-s…

A Python toolkit/library for reality-centric machine/deep learning and data mining on partially-observed time series, including SOTA neural network models for scientific analysis tasks of imputatio…

tsl: a PyTorch library for processing spatiotemporal data.

Official repository for the paper "Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks" (ICLR 2022)

A Dual Framework for Low-rank Tensor Completion