XMAHA

XMAHA's Stars

• DatrikIntelligence/D3A-TS-Denoising-Driven-Data-Augmentation-in-Time-Series

  This repository contains all the source code needed to reproduce the experiments or review the results obtained in the research paper "D3A-TS: Denoising-Driven Data Augmentation in Time Series"

  Language:TypeScript5

• DatrikIntelligence/Stacked-DCNN-RUL-PHM21

  Source code of the paper "A stacked DCNN to predict the RUL of a turbofan engine", third place ranked in the PHM21 data challenge.

  Language:Python729

• alovberg/tiny-N-CMAPSS

  A tiny version of the 2021 PHM Data Challenge dataset (a subset of the N-CMAPSS dataset)

  Language:Jupyter Notebook5

• Adithya-Thonse/N-CMAPSS_DL

  Estimating Remaining Useful Life of a Turbofan Jet Engine using NCMAPSS dataset

  Language:Jupyter Notebook195

• wehos/awesome-graph-transformer

  Papers about graph transformers.

  75464

• snap-stanford/pretrain-gnns

  Strategies for Pre-training Graph Neural Networks

  Language:Python955161

• lucidrains/vit-pytorch

  Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

  Language:Python19.5k3k

• qingsongedu/time-series-transformers-review

  A professionally curated list of awesome resources (paper, code, data, etc.) on transformers in time series.

  2.4k235

• Liudd-BJUT/WT-planetary-gearbox-dataset

  WT-planetary-gearbox-datasets

  4720

• yanshen0210/LiConvFormer-a-lightweight-fault-diagnosis-framework

  一种轻量化故障诊断框架——LiConvFormer

  Language:Python5713

• ChenQian0618/TFN

  this is the open code of paper entitled "TFN: An Interpretable Neural Network With Time Frequency Transform Embedded for Intelligent Fault Diagnosis".

  Language:Python8212

• rz0718/MHMS_DEEPLEARNING

  Language:OpenEdge ABL9243

• ZhaoZhibin/Probabilistic_RUL_Prediction

  Language:Python554

• XGraph-Team/Spectral-Graph-Survey

  first commit

  Language:Python1783

• divelab/DIG

  A library for graph deep learning research

  Language:Python1.8k281

• EdisonLeeeee/SpikeNet

  [AAAI 2023] Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks

  Language:Python557

• implus/PytorchInsight

  a pytorch lib with state-of-the-art architectures, pretrained models and real-time updated results

  Language:Python856122

• xmu-xiaoma666/External-Attention-pytorch

  🍀 Pytorch implementation of various Attention Mechanisms, MLP, Re-parameter, Convolution, which is helpful to further understand papers.⭐⭐⭐

  Language:Python11.3k1.9k

• google-research/scenic

  Scenic: A Jax Library for Computer Vision Research and Beyond

  Language:Python3.2k426

• sxu-yaokx/MAGCN

  Language:Python264

• IMKBLE/MAGCN

  Language:Python362

• xdweixia/NN-2022-MVGC

  Tensorflow 1.13.X implementation for our NN paper: Wei Xia, Sen Wang, Ming Yang, Quanxue Gao, Jungong Han, Xinbo Gao: Multi-view graph embedding clustering network: Joint self-supervision and block diagonal representation. Neural Networks 145: 1-9 (2022)

  Language:Python161

• cathysiyu/Mechanical-datasets

  220107

• mohamedr002/AMDA

  This is an implementation of single source multiple target domain adaptation for fault diagnosis

  Language:Jupyter Notebook4215

• pgmpy/pgmpy

  Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

  Language:Python2.7k699

• ncullen93/pyBN

  Bayesian Networks in Python

  Language:Python14655

• FenTechSolutions/CausalDiscoveryToolbox

  Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.

  Language:Python1.1k197

• LirongWu/awesome-graph-self-supervised-learning

  Code for TKDE paper "Self-supervised learning on graphs: Contrastive, generative, or predictive"

  1.3k166

• AnthonySong98/awesome-graph-based-semi-supervised-learning

  A collection of resources for graph-based semi-supervised learning (GSSL).

  172

• HazeDT/PHMGNNBenchmark

  this code library is mainly about applying graph neural networks to intelligent diagnostic and prognostic.

  Language:Python20144