fengkoushangdeZZX

AdaTime

Official implementation of AdaTime: A Benchmarking Suite for Domain Adaptation on Time Series Data

ADBench

Official Implement of "ADBench: Anomaly Detection Benchmark".

AIJack

reveal the vulnerabilities of machine learning models

ALDA

Code for "Adversarial-Learned Loss for Domain Adaptation"(AAAI2020) in PyTorch.

AMSGradP

Imporved AdamP called AMSGradP is suitable for intelligent fault diagnosis (故障诊断)

Awesome-Dataset-Distillation

Awesome Dataset Distillation Papers

Awesome-time-series

A comprehensive survey on the time series domains

awesome-transformer-search

A curated list of awesome resources combining Transformers with Neural Architecture Search

bayesian-deep-rul

Bayesian Deep Learning for Remaining Useful Life Estimation of Machine Tool Components

Bearing-Fault-Diagnosis-CNNs-and-Vibration-Spectrogram

Vibrational Analysis for CWRU Bearing Dataset

calda

Contrastive Adversarial Learning for Multi-Source Time Series Domain Adaptation

Codes

Conditional-Adversarial-Domain-Generalization-with-Single-Discriminator

Pytorch implementation of the paper: "Conditional Adversarial Domain Generalization With a Single Discriminator for Bearing Fault Diagnosis"

DeepFD

The repository of "DeepFD: Automated Fault Diagnosis and Localization for Deep Learning Programs"

deeptime-notebooks

Notebooks which contain example code for deeptime, used to render the documentation.

EdgeKE

Code for paper "EdgeKE: An On-Demand Deep Learning IoT System for Cognitive Big Data on Industrial Edge Devices"

External-Attention-pytorch

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

GANF

Offical implementation of "Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series" (ICLR 2022)

GNNApp-Papers

Listing the research works related to risk control based on GNN and it interpretability. 1. we can learn the application of GNN in risk control (including fraud detection). 2. For possible prediction, we can use the interpretability of GNN to explaine how can we get such results.

GRDA

industry-machine-learning

A curated list of applied machine learning and data science notebooks and libraries across different industries (by @firmai)

mtad-gat-pytorch

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

paper-reading

深度学习经典、新论文逐段精读

pyod

(JMLR' 19) A Python Toolbox for Scalable Outlier Detection (Anomaly Detection)

qlib

Qlib is an AI-oriented quantitative investment platform, which aims to realize the potential, empower the research, and create the value of AI technologies in quantitative investment. With Qlib, you can easily try your ideas to create better Quant investment strategies. An increasing number of SOTA Quant research works/papers are released in Qlib.

Structure-Learning-from-Time-series-Data-with-CausalNex

Time series data structure learning with NOTEARS and DYNOTEARS

T-GCN

Temporal Graph Convolutional Network for Urban Traffic Flow Prediction Method

time-series-autoencoder

:chart_with_upwards_trend: PyTorch dual-attention LSTM-autoencoder for multivariate Time Series :chart_with_upwards_trend:

tods

TODS: An Automated Time-series Outlier Detection System

TS-TCC

[IJCAI-21] "Time-Series Representation Learning via Temporal and Contextual Contrasting"