renxiaoxing's starred repositories
annotated_deep_learning_paper_implementations
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
100-Days-Of-ML-Code
100 Days of ML Coding
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
Dive-into-DL-PyTorch
本项目将《动手学深度学习》(Dive into Deep Learning)原书中的MXNet实现改为PyTorch实现。
best-of-ml-python
🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.
BayesianOptimization
A Python implementation of global optimization with gaussian processes.
handson-ml3
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.
introduction_to_ml_with_python
Notebooks and code for the book "Introduction to Machine Learning with Python"
graph-based-deep-learning-literature
links to conference publications in graph-based deep learning
GraphEmbedding
Implementation and experiments of graph embedding algorithms.
Machine-Learning-with-Python
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
GraphNeuralNetwork
《深入浅出图神经网络：GNN原理解析》配套代码
GNN-Recommender-Systems
An index of recommendation algorithms that are based on Graph Neural Networks. (TORS)
Gaussian-Process-Regression-Tutorial
An Intuitive Tutorial to Gaussian Processes Regression
comet-examples
Examples of Machine Learning code using Comet.ml
Graph_Neural_Network_Learning
图神经网络(图卷积网络) 个人学习总结
Surrogate_Optimization
A step-by-step guide for surrogate optimization using Gaussian Process surrogate model
DEN-ARMOEA
# Introduction of DNN-AR-MOEA This repository contains code necessary to reproduce the experiments presented in Evolutionary Optimization of High-DimensionalMulti- and Many-Objective Expensive ProblemsAssisted by a Dropout Neural Network. Gaussian processes are widely used in surrogate-assisted evolutionary optimization of expensive problems. We propose a computationally efficient dropout neural network (EDN) to replace the Gaussian process and a new model management strategy to achieve a good balance between convergence and diversity for assisting evolutionary algorithms to solve high-dimensional multi- and many-objective expensive optimization problems. mainlydue to the ability to provide a confidence level of their outputs,making it possible to adopt principled surrogate managementmethods such as the acquisition function used in Bayesian opti-mization. Unfortunately, Gaussian processes become less practi-cal for high-dimensional multi- and many-objective optimizationas their computational complexity is cubic in the number oftraining samples. # References If you found DNN-AR-MOEA useful, we would be grateful if you cite the following reference: Evolutionary Optimization of High-DimensionalMulti- and Many-Objective Expensive ProblemsAssisted by a Dropout Neural Network (IEEE Transactions on Systems, Man and Cybernetics: Systems).
Gaussian-Process
Implementing a Gaussian Process regression model from scratch
surrogate-cmaes
Surrogate CMA-ES (S-CMA-ES and DTS-CMA-ES) is a surrogate-based optimizing evolution strategy. It is based on the N. Hansen's CMA-ES algorithm which is interconnected with Gaussian processes (or random forests, that are, however, not maintained here anymore).
soft_systems_course
Course on computational design, non-linear optimization, and dynamics of soft systems at UIUC.
epsilon-greedyGPR
Accelerating the Evolutionary Algorithms by Gaussian Process Regression with epsilon-greedy acquisition function