Stars
Repository for the results of my master thesis, about the generation and evaluation of synthetic data using GANs
Generation of Time Series data using generatuve adversarial networks (GANs) for biological purposes.
T81-558: Keras - Applications of Deep Neural Networks @Washington University in St. Louis
Semi-Recurrent CNN-based VAE-GAN for Sequential Data Generation
ACDWM (Adaptive Chunk-based Dynamic Weighted Majority)
Spectral Temporal Graph Neural Network (StemGNN in short) for Multivariate Time-series Forecasting
Water Quality Management and Forecasting System(水质管理与预测系统)
[TPAMI 2024] Awesome Resources of GNNs for Time Series Analysis (GNN4TS)
Official repository for the paper "Filling the G_ap_s: Multivariate Time Series Imputation by Graph Neural Networks" (ICLR 2022)
基于Springboot+Vue+Python深度神经网络学习算法水质管理预测系统设计毕业源码案例设计
Conditional GAN for generating synthetic tabular data.
transformer/self-attention for Multidimensional time series forecasting 使用transformer架构实现多维时间预测
A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.
Codebase for Time-series Generative Adversarial Networks (TimeGAN) - NeurIPS 2019
Implementation of seq2seq with attention in keras
About Code release for "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight), https://openreview.net/forum?id=LzQQ89U1qm_
Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs (CVPR 2022)
Let's train vision transformers (ViT) for cifar 10!
The GitHub repository for the paper "Informer" accepted by AAAI 2021.
Seq2Seq, Bert, Transformer, WaveNet for time series prediction.
Implementation of Transformer model (originally from Attention is All You Need) applied to Time Series.
proof of concept for a transformer-based time series prediction model
A tutorial demonstrating how to implement deep learning models for time series forecasting
Shared repository for open-sourced projects from the Google AI Language team.
使用多种算法（线性回归、随机森林、支持向量机、BP神经网络、GRU、LSTM）进行电力系统负荷预测/电力预测。通过一个简单的例子。A variety of algorithms (linear regression, random forest, support vector machine, BP neural network, GRU, LSTM) are used for power …
Using BERT+Bi-LSTM+CRF