Pinned Repositories
adda
atom
:atom: The hackable text editor
awesome-AIOps
AIOps学习资料汇总，欢迎一起补全这个仓库，欢迎star
awesome-graph-classification
A collection of important graph embedding, classification and representation learning papers with implementations.
awsome-domain-adaptation
A collection of AWESOME things about domian adaptation
bigdata18
Transfer learning for time series classification
CausalDiscoveryToolbox
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
DSN
Pytorch implementation of Domain Separation Networks
mwb_deeplog
SpringBoot-Dubbo-Docker-Jenkins
基于SpringBoot+Dubbo的微服务框架（借助Docker+Jenkins实现自动化、容器化部署）
wjj5881005's Repositories
wjj5881005/awesome-graph-classification
A collection of important graph embedding, classification and representation learning papers with implementations.
wjj5881005/CausalDiscoveryToolbox
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
wjj5881005/CausalInference.jl
Causal inference, graphical models and structure learning with the PC algorithm.
wjj5881005/DeepCTR
Easy-to-use,Modular and Extendible package of deep-learning based CTR models .
wjj5881005/density_ratio_estimation
This is an implementation of direct density ratio estimation by unconstrained Least-Squares Importance Fitting (uLSIF) with python.
wjj5881005/DGMR
Skilful precipitation nowcasting using deep generative models of radar
wjj5881005/e-book-collections
Some e-books I have read and recommended.
wjj5881005/fewshot_absa
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis
wjj5881005/GDN
Implementation code for the paper "Graph Neural Network-Based Anomaly Detection in Multivariate Time Series"
wjj5881005/GNNetworkingChallenge
RouteNet baseline for the Graph Neural Networking Challenge (https://bnn.upc.edu/challenge/)
wjj5881005/Gradient-Free-Optimizers
Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.
wjj5881005/GraphEmbedding
Implementation and experiments of graph embedding algorithms.
wjj5881005/Hyperactive
A hyperparameter optimization and data collection toolbox for convenient and fast prototyping of machine-learning models.
wjj5881005/isolation-forest
A Spark/Scala implementation of the isolation forest unsupervised outlier detection algorithm.
wjj5881005/lifetimes
Lifetime value in Python
wjj5881005/luminol
Anomaly Detection and Correlation library
wjj5881005/metis
Interpreting Deep Learning-Based Networking Systems (SIGCOMM 2020)
wjj5881005/mixture-of-experts
PyTorch Re-Implementation of "The Sparsely-Gated Mixture-of-Experts Layer" by Noam Shazeer et al. https://arxiv.org/abs/1701.06538
wjj5881005/ML_Notes
机器学习算法的公式推导以及numpy实现
wjj5881005/nitime
Timeseries analysis for neuroscience data
wjj5881005/prml
Repository of notes, code and notebooks for the book Pattern Recognition and Machine Learning by Christopher Bishop
wjj5881005/PRML-1
PRML algorithms implemented in Python
wjj5881005/pytorch-forecasting
Time series forecasting with PyTorch
wjj5881005/PyTorch-Tutorial
Build your neural network easy and fast, 莫烦Python中文教学
wjj5881005/pytorch_geometric_temporal
PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)
wjj5881005/PyTorch_Tutorial
《Pytorch模型训练实用教程》中配套代码
wjj5881005/scikit-optimize
Sequential model-based optimization with a `scipy.optimize` interface
wjj5881005/skggm
Scikit-learn compatible estimation of general graphical models
wjj5881005/spark-iforest
Isolation Forest on Spark
wjj5881005/tigramite
Tigramite is a time series analysis python module for causal discovery. The Tigramite documentation is at