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ML & DL

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Active Learning

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Core-set Selection

Diversity-based Sampling

Expected Error Reduction

Expected Model Change

Query-by-Committee

Uncertainty Sampling

Imitation Learning

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Behavior Cloning

DAgger

Generative Adversarial Imitation Learning (GAIL)

Inverse Reinforcement Learning (IRL)

Maximum Entropy IRL

Reinforcement Learning

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Bandit

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ε-Greedy

Multi-Armed Bandit

Thompson Sampling

Upper Confidence Bound (UCB)

Deep Reinforcement Learning

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Deep Q-Network (DQN)

MuZero

Proximal Policy Optimization (PPO)

Soft Actor-Critic (SAC)

Dynamic Programming

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Policy Evaluation

Policy Iteration

Value Iteration

关键对象

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Action

Environment Model

Policy

Reward

State

Value Function

Monte Carlo RL

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Monte Carlo Control

Monte Carlo Prediction

Temporal Difference Learning

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Q-Learning

SARSA

TD(0)

Self-supervised Learning

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自回归预测

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GPT-style Next-token Prediction

n-gram Language Model

RNN Language Model

Transformer Language Model

对比学习

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Barlow Twins

BYOL

CLIP-style Image-Text Contrastive Learning

MoCo

SimCLRbi

SwAV

Word2Vec

去噪学习

总览

BART-style Denoising

Denoising Autoencoder

Diffusion Denoising Objective

掩码预测

总览

BERT-style Pretraining

Masked Autoencoder (MAE)

Masked Image Modeling

Masked Language Modeling (MLM)

预测式表示学习

总览

Bootstrap Representation Learning

Contrastive Predictive Coding (CPC)

Semi-supervised Learning

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Consistency Regularization

FixMatch

Pseudo-labeling

Supervised Learning

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分类模型

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AdaBoost

CNN Classifier

Decision Tree

k-Nearest Neighbors (kNN)

Logistic Regression

Multilayer Perceptron (MLP)

Naive Bayes

Random Forest

RNN Classifier

Softmax Regression

Support Vector Machine (SVM)

Transformer Classifier

回归模型

总览

Elastic Net

Gaussian Process Regression

Gradient Boosting Regression

Lasso Regression

Linear Regression

Neural Network Regression

Random Forest Regression

Ridge Regression

Support Vector Regression (SVR)

Unsupervised Learning

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聚类方法

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Gaussian Mixture Model (GMM；常用 EM 算法训练)

K-Means

Spectral Clustering

密度估计

总览

Gaussian Mixture Model (GMM)

Kernel Density Estimation (KDE)

降维方法

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Principal Component Analysis (PCA)

异常检测

ML & DL Resources

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State

本页用于记录 State 的定义、基本思想、适用场景和相关链接。

所属路径

Reinforcement Learning / 关键对象 / State

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