Technology

Artificial Intelligence Glossary

This glossary currently includes a total of 743 specialized AI terms based on machine learning concepts and terminologies.

This glossary currently includes a total of 743 specialized AI terms based on machine learning concepts and terminologies. Synced will continue to update the collection. Vocabulary updates proceed in two phases: 1) we will extract terminologies from textbooks or other credible academic sources; 2) we will extract uncommon terms found in compiled essays or other sources into the glossary.

Reader feedbacks and suggestions are vitally important for the success of this project, if you have any suggestions please don’t hesitate to let us know in the comment section! To read the glossary in Chinese please see 机器之心翻译红宝书 or visit us on GitHub!)

 

A

Absolute Value Rectification
Activation Function
Accumulated Error Backpropagation
Acquisition Function
Actor-Critic Method
Adaptive Bitrate (ABR) Algorithm
Adaptive Resonance Theory/ART
Addictive Model
Adversarial Example
Adversarial Networks
Affine Layer
Affinity Matrix
Agent
Algorithm
Alpha-Beta Pruning
Ancestral Sampling
Annealed Importance Sampling
Anomaly Detection
Application-Specific Integrated Circuit
Approximate Bayesian Computation
Approximate Inference
Approximation
Architecture
Area Under ROC Curve/AUC
Artificial General Intelligence/AGI
Artificial Intelligence/AI
Association Analysis
Asymptotically Unbiased
Asynchoronous Stochastic Gradient Descent
Attention Mechanism
Attribute Conditional Independence Assumption
Attribute Space
Attribute Value
Augmented Lagrangian
Autoencoder
Automatic Differentiation
Automatic Speech Recognition/ASR
Automatic Summarization
Auto-Regressive Network
Average Gradient
Average-Pooling


B

Backpropagation/BP
Backpropagation Through Time
Bag of Words/BoW
Base Learner
Base Learning Algorithm
Batch
Batch Normalization/BN
Bayes Decision Rule
Bayes Error
Bayes Model Averaging/BMA
Bayes Optimal Classifier
Bayesian Decision Theory
Bayesian Network
Bayesian Optimization
Beam Search
Bechmark
Belief Network
Between-Class Scatter Matrix
Bias
Biased
Biased Importance Sampling
Bias-Variance Decomposition
Bias-Variance Dilemma
Bi-Directional Long-Short Term Memory/Bi-LSTM
Binary Classification
Binary Relation
Binary Sparse Coding
Binomial Distribution
Binomial Test
Bi-Partition
Block Coordinate Descent
Block Gibbs Sampling
Boilerplate Code
Boltzmann Distribution
Boltzmann Machine
Bootstrap Sampling
Bootstrapping
Break-Event Point/BEP
Bridge Sampling
Broadcasting
Burning-In


C

Calculus of Variations
Calibration
Cascade/Coalesced
Cascade-Correlation
Categorical Attribute
Categorical Distribution
Causal Factor
Causal Modeling
Centered Difference
Central Limit Theorem
Chain Rule
Chordal Graph
Class-Conditional Probability
Classification and Regression Tree/CART
Classifier
Class-Imbalance
Clip Gradient
Clique Potential
Closed-Form
Cluster
Cluster Analysis
Clustering
Clustering Ensemble
Co-Adapting
Coding Matrix
Collaborative Filtering
COLT
Committee-Based Learning
Competitive Learning
Complete Graph
Component Learner
Comprehensibility
Computation Cost
Computational Linguistics
Computer Vision
Concept Drift
Concept Learning System/CLS
Conditional Entropy
Conditional Mutual Information
Conditional Probability Table/CPT
Conditional Random Field/CRF
Conditional Risk
Confidence
Confusion Matrix
Conjugate Directions
Conjugate Distribution
Conjugate Gradient
Connection Weight
Connectionism
Consistency
Consistency Convergence
Contingency Table
Continuation Method
Continuous Attribute
Contractive Autoencoder
Contractive Neural Network
Convex Optimization
Convergence
Conversational Agent
Convex Optimization
Convex Quadratic Programming
Convexity
Convolutional Boltzmann Machine
Convolutional Neural Network/CNN
Co-Occurrence
Coordinate Descent
Correlation Coefficient
Cosine Similarity
Cost Curve
Cost Function
Cost Matrix
Cost-Sensitive
Covariance
Covariance Matrix
Cross Entropy
Cross Validation
Cross-Correlation
Crowdsourcing
Cumulative Function
Curse of Dimensionality
Curve-Fitting
Cut Point
Cutting Plane Algorithm


D

Data Generating Distribution
Data Mining
Data Parallelism
Data Set
Data Wrangling
Dataset Augmentation
Decision Boundary
Decision Stump
Decision Tree
Deconvolutional Network
Deduction
Deep Belief Network
Deep Boltzmann Machine
Deep Circuit
Deep Convolutional Generative Adversarial Network/DCGAN
Deep Generative Model
Deep Learning
Deep Neural Network/DNN
Deep Q-Learning
Deep Q-Network
Denoising Autoencoder
Denoising Score Matching
Density Estimation
Density-Based Clustering
Detailed Balance
Determinant
Diagonal Matrix
Differentiable Neural Computer
Differential Entropy
Differential Equation
Dimensionality Reduction Algorithm
Directed Edge
Directed Graphical Model
Directional Derivative
Dirichlet Distribution
Disagreement Measure
Discriminative Model
Discriminator
Discriminator Network
Distance Measure
Distance Metric Learning
Distribution
Divergence
Diversity Measure
Domain Adaption
Double Backprop
Doubly Block Circulant Matrix
Downsampling
D-Separation/Directed Separation
Dual Problem
Dummy Node
Dynamic Fusion
Dynamic Programming


E

Echo State Network
Edge Device
Eigendecomposition
Eigenvalue
Eigenvalue Decomposition
Element-Wise Product
Ellipsoid Method
Embedding
Emotional Analysis
Empirical Conditional Entropy
Empirical Entropy
Empirical Error
Empirical Risk
End-to-End
Energy-Based Model
Ensemble Learning
Ensemble Pruning
Epochs
Error Correcting Output Codes/ECOC
Error Rate
Error-Ambiguity Decomposition
Euclidean Distance
Euclidean Norm
Evolutionary Computation
Expectation-Maximization/EM
Expected Loss
Expert Network
Explaining Away Effect
Exploding Gradient Problem
Exponential Loss Function
Extreme Learning Machine/ELM


F

Factor Analysis
Factorization
Factors of Variation
False Negative
False Positive
False Positive Rate/FPR
Fault-Tolerant Asynchronous Training
Feature Engineering
Feature Extractor
Feature Map
Feature Selection
Feature Vector
Featured Learning
Feedforward Neural Networks/FNN
Field Programmable Gated Array
Fine-Tuning
Finite Difference
Fixed Point Equation
Flipping Output
Fluctuation
Forget Gate
Forward Stagewise Algorithm
Fourier Transform
Frequentist
Frequentist Probability
Full-Rank Matrix
Functional Derivative
Functional Neuron


G

Gain Ratio
Game Theory
Gated Recurrent Net/GRN
Gaussian Kernel function
Gaussian Mixture Model
Gaussian Process
General Problem Solving
Generalization
Generalization Error
Generalization Error Bound
Generalized Lagrange Function
Generalized Linear Model
Generalized Pseudolikelihood
Generalized Rayleigh Quotient
Generalized Score Matching
Generative Adversarial Networks/GAN
Generative Model
Generative Moment Matching Network
Generator
Genetic Algorithm/GA
Giant Magnetoresistance
Gibbs Sampling
Gini Index
Global Contrast Normalization
Global Minimum
Global Optimization
Gradient Boosting Tree
Gradient Descent
Gradient Energy Distribution
Graph Theory
Grid Search
Ground-Truth


H

Hard Margin
Hard Voting
Harmonic Mean
Hesse Matrix
Heterogeneous Information Network/HIN
Hidden Dynamic Model
Hidden Layer
Hidden Markov Model/HMM
Hierarchical Clustering
Hilbert Space
Hinge Loss Function
Hold-Out
Homogeneous
Hybrid Computing
Hyperparameter
Hypothesis
Hypothesis Test


I

ICML
Identity Matrix
Image Restoration
Improved Iterative Scaling/IIS
Incremental Learning
Independent and Identically Distributed/i.i.d.
Independent Component Analysis/ICA
Independent Subspace Analysis
Indicator Function
Individual Learner
Induction
Inductive Bias
Inductive Learning
Inductive Logic Programming/ILP
Inequality Constraint
Inference
Information Entropy
Information Gain
Input Layer
Insensitive Loss
Inter-Cluster Similarity
International Conference for Machine Learning/ICML
Intra-Cluster Similarity
Intrinsic Value
Invariance
Invert
Isometric Mapping/Isomap
Isotonic Regression
Iterative Dichotomiser


J

Jensen-Shannon Divergence/JSD


K

Kernel Method
Kernel Trick
Kernelized Linear Discriminant Analysis/KLDA
K-Fold Cross Validation
K-Means Clustering
K-Nearest Neighbours Algorithm/KNN
Knowledge Base
Knowledge Graph
Knowledge Representation


L

Label Space
Lagrange Duality
Lagrange Multiplier
Laplace Smoothing
Laplacian Correction
Latent Dirichlet Allocation/LDA
Latent Semantic Analysis
Latent Variable
Law of Large Number
Layer-Wise Adaptive Rate Scaling/LARS
Lazy Learning
Leaky ReLU
Learner
Learning by Analogy
Learning Rate
Learning Vector Quantization/LVQ
Least Squares Regression Tree
Leave-One-Out/LOO
Lebesgue-Integrable
Left Eigenvector
Leibniz’s Rule
Linear Discriminant Analysis/LDA
Linear Model
Linear Regression
Linear Threshold Units
Link Function
Local Conditional Probability Distribution
Local Contrast Normalization
Local Curvature
Local Markov Property
Local Minimum
Log Likelihood
Log Odds/Logit
Logistic Regression
Log-Likelihood
Log-Linear Regression
Long-Short Term Memory/LSTM
Long-Term Dependency
Loopy Belief Propagation
Loss Function
Low Rank Matrix Approximation


M

Machine Translation/MT
Macron-P
Macron-R
Main Diagonal
Majority Voting
Manifold Assumption
Manifold Learning
Manifold Tangent Classifier
Margin Theory
Marginal Distribution
Marginal Independence
Marginal Probability Distribution
Marginalization
Markov Chain
Markov Chain Monte Carlo/MCMC
Markov Random Field
Matrix Inversion
Maximal Clique
Maximum A Posteriori
Maximum Likelihood Estimation/MLE
Maximum Margin
Maximum Weighted Spanning Tree
Max-Pooling
Mean Product of Student T-Distribution
Mean Squared Error
Mean-Covariance Restricted Boltzmann Machine
Measure Theory
Meta-Learner
Metric Learning
Micro-P
Micro-R
Mini-Batch SGD
Minimal Description Length/MDL
Minimax Game
Misclassification Cost
Mixture Density Network
Mixture of Experts
Model Predictive Control (MPC)
Moment Matching
Momentum
Moral Graph
Multi-Class Classification
Multi-Document Summarization
Multi-Kernel Learning
Multi-Layer Feedforward Neural Networks
Multilayer Perceptron/MLP
Multimodal Learning
Multinomial Distribution
Multiple Dimensional Scaling
Multiple Linear Regression
Multi-response Linear Regression/MLR
Multivariate Normal Distribution
Mutual Information


N

Naive Bayes
Naive Bayes Classifier
Named Entity Recognition
Nash Equilibrium
Natural Language Generation/NLG
Natural Language Processing/NLP
Nearest-Neighbor Search
Negative Class
Negative Correlation
Negative Definite
Negative Log Likelihood
Negative Semidefinite
Neighbourhood Component Analysis/NCA
Neural Machine Translation
Neural Turing Machine
Neuromorphic Computing
Newton Method
Conference on Neural Information Processing Systems/NIPS
No Free Lunch Theorem/NFL
Noise-Contrastive Estimation
Nominal Attribute
Non-Convex Optimization
Nonlinear Model
Non-Linear Oscillation
Non-Metric Distance
Non-Negative Matrix Factorization
Non-Ordinal Attribute
Non-Saturating Game
Norm
Normalization
Nuclear Norm
Numerical Attribute
Numerical Optimization


O

Objective Function
Oblique Decision Tree
Occam’s Razor
Odds
Offline Inference
Off-Policy
One Shot Learning
One-Dependent Estimator/ODE
Online Inference
On-Policy
Orthogonal Matrix
Orthonormal
Outlier
Out-of-Bag Estimate
Output Layer
Output Smearing
Overcomplete
Overfitting
Oversampling


P

Paired T-Test
Pairwise
Pairwise Markov Property
Parallel Tempering
Parameter
Parameter Estimation
Parameter Server
Parameter Tuning
Parse Tree
Partial Derivative
Particle Swarm Optimization/PSO
Perceptron
Performance Measure
Permutation Invariant
Perplexity
Plug and Play Generative Network
Plurality Voting
Polarity Detection
Polynomial Kernel Function
Pooling
Positive Class
Positive Definite Matrix
Posterior Inference
Posterior Probability
Post-Hoc Test
Post-Pruning
Potential Function
Power Method
Precision
Prepruning
Principal Component Analysis/PCA
Principle of Multiple Explanations
Prior Knowledge
Probability Graphical Model
Proximal Gradient Descent/PGD
Pruning
Pseudo-Label


Q

Quadratic Programming
Quantized Neural Network/QNN
Quantum Computer
Quantum Computing
Quasi Newton Method


R

Radial Basis Function/RBF
Random Forest Algorithm
Random Walk
Recall
Receiver Operating Characteristic/ROC
Rectified Linear Unit/ReLU
Recurrent Neural Network
Reference Model
Regression
Regularization
Regularizer
Reinforcement Learning/RL
Relative Entropy
Reparametrization
Representation Learning
Representer Theorem
Reproducing Kernel Hilbert Space/RKHS
Re-Sampling
Rescaling
Reservoir Computing
Residual Mapping
Residual Network
Restricted Boltzmann Machine/RBM
Restricted Isometry Property/RIP
Reverse Mode Accumulation
Re-Weighting
Ridge Regression
Robustness
Root Mode
Rule Engine
Rule Learning


S

Saddle Point
Saddle-Free Newton Method
Sample Space
Sampling
Score Function
Second Derivative
Second-Order Method
Self-Contrastive Estimation
Self-Driving
Self-Organizing Map/SOM
Semantic Hashing
Semantic Similarity
Semi-Definite Programming
Semi-Naive Bayes Classifiers
Semi-Restricted Boltzmann Machine
Semi-Supervised Learning
Semi-Supervised Support Vector Machine
Sentiment Analysis
Separating Hyperplane
Shannon Entropy
Sigmoid Function
Similarity Measure
Simulated Annealing
Simultaneous Localization and Mapping/SLAM
Singular Value
Singular Value Decomposition
Slack Variables
Slowness Principle
Smoothing
Smoothness Prior
Soft Margin
Soft Margin Maximization
Soft Voting
Sparse Activation
Sparse Coding
Sparse Connectivity
Sparse Initialization
Sparse Representation
Sparsity
Specialization
Spectral Clustering
Spectral Radius
Speech Recognition
Spiking Neural Nets
Splitting variable
Squashing function
Stability-plasticity dilemma
Stacked Deconvolutional Network/SDN
Standard Deviation
Stationary Distribution
Stationary Point
Statistical Learning
Status Feature Function
Stochastic Gradient Descent
Stochastic Matrix
Stochastic Maximum Likelihood
Stochastic Neighbor Embedding
Stratified Sampling
Structural Risk
Structural Risk Minimization/SRM
Structured Variational Inference
Subspace
Supervised Learning
Support Vector Expansion
Support Vector Machine/SVM
Surrogat Loss
Surrogate Function
Symbolic Learning
Symbolism
Synset
Synthetic Feature


T

Tangent Plane
Tangent Prop
T-Distribution Stochastic Neighbour Embedding/t-SNE
Tempered Transition
Tensor
Tensor Processing Units/TPU
The Least Square Method
Threshold
Threshold Logic Unit
Threshold-Moving
Tiled Convolution
Time Delay Neural Network
Time Step
Tokenization
Training Error
Training Instance
Transductive Learning
Transfer Learning
Treebank
Tria-By-Error
Triangulate
Trigram
True Negative
True Positive
True Positive Rate/TPR
Turing Machine
Twice-Learning
Two-Dimensional Array


U

Underestimation
Underfitting
Undersampling
Understandability
Undirected Graphical Model
Unequal Cost
Unit Norm
Unit Variance
Unitary Matrix
Unit-Step Function
Univariate Decision Tree
Unprojection
Unshared Convolution
Unsupervised Learning
Unsupervised Layer-Wise Training
Upper Confidence Bounds
Upsampling


V

Vanishing Gradient Problem
Variational Derivative
Variational Free Energy
Variational Inference
VC Theory
Version Space
Virtual Adversarial Example
Viterbi Algorithm
Von Neumann Architecture


W

Weak Learner
Weight
Weight Sharing
Weighted Voting
Wasserstein GAN/WGAN
Within-Class Scatter Matrix
Word Embedding
Word Sense Disambiguation


X


Y


Z

Zero Mean
Zero-Data Learning
Zero-Shot Learning

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