floricaaa

tf-pytorch-paddle

💥三大深度学习框架：tensorflow，pytorch，paddle的高层API使用学习

intro_dgm

"Deep Generative Modeling": Introductory Examples

awesome-latex-drawing

Drawing Bayesian networks, graphical models, tensors, technical frameworks, and illustrations in LaTeX.

timeseries-generator

A library to generate synthetic time series data by easy-to-use factors and generator

benchmark_VAE

Unifying Variational Autoencoder (VAE) implementations in Pytorch (NeurIPS 2022)

dimensionality_reduction_alo_codes

特征提取/数据降维:PCA、LDA、MDS、LLE、TSNE等降维算法的python实现

crossnorm-selfnorm

CrossNorm and SelfNorm for Generalization under Distribution Shifts, ICCV 2021

GraphSAGE_RL

Advancing GraphSAGE with A Data-driven Node Sampling

GraphSAGE

Representation learning on large graphs using stochastic graph convolutions.

GNN-algorithms

图神经网络相关算法详述及实现

GraphNeuralNetwork

Implementation and experiments of graph neural netwokrs, like gcn,graphsage,gat,etc.

graph_nets

PyTorch Implementation and Explanation of Graph Representation Learning papers: DeepWalk, GCN, GraphSAGE, ChebNet & GAT.

gcn

Implementation of Graph Convolutional Networks in TensorFlow

tf_geometric

Efficient and Friendly Graph Neural Network Library for TensorFlow 1.x and 2.x

MMD_Loss.Pytorch

A pytorch implementation of Maximum Mean Discrepancies(MMD) loss

KL-CPD

Kernel Learning with Maximum Mean Discrepancy for Detecting Time Series Change Points

awesome-digital-twin

MDD

Code released for ICML 2019 paper "Bridging Theory and Algorithm for Domain Adaptation".

AdaIN-style

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

pyGAT

Pytorch implementation of the Graph Attention Network model by Veličković et. al (2017, https://arxiv.org/abs/1710.10903)

knowledge_graph_attention_network

KGAT: Knowledge Graph Attention Network for Recommendation, KDD2019

UDTL

Source codes for the paper "Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study" published in TIM

DAGCN

This code is about the implementation of Domain Adversarial Graph Convolutional Network for Fault Diagnosis Under Variable Working Conditions.

anomaly-detection-resources

Anomaly detection related books, papers, videos, and toolboxes

Adaboost

Implementations of the classification algorithm "Adaboost" with various weak learners

Superstatistics

Combinatorial Superstatistics for Soft QCD, Möbius Inversion [arXiv:1910.06279]

Superstatistics

Collection of functions to carry out superstatistical analysis of a given data set

TransGAN

[NeurIPS‘2021] "TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up", Yifan Jiang, Shiyu Chang, Zhangyang Wang

nasa_bearing_abnormal_detection

bearing-fault-detection

Improving on NASA's work with induction motor bearing fault detection using RNN-powered smart sensors.