| |
Accept-Reject Sampling Method | |
Accumulated Error Backpropagation | |
| |
| |
| |
| |
| |
Adaptive Bitrate Algorithm | |
| |
Adaptive Gradient Algorithm | |
Adaptive Moment Estimation Algorithm | |
Adaptive Resonance Theory | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Artificial Neural Network | |
| |
| |
| |
| |
| |
| |
Automatic Differentiation | |
| |
| |
Back Propagation Algorithm | |
Back Propagation Through Time | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| |
| |
| |
| |
| |
| |
Between-Class Scatter Matrix | |
| |
| |
| |
| |
| |
Bias-Variance Decomposition |
|
| |
Bidirectional Recurrent Neural Network | |
| |
Bilingual Evaluation Understudy | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Classification And Regression Tree | |
| |
|
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Computational Learning Theory | |
| |
| |
Conditional Probability Distribution |
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| |
| |
| |
| |
Convex Quadratic Programming | |
| |
| |
Convolutional Neural Network | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Deep Convolutional Generative Adversarial Network | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Distributed Representation | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Empirical Conditional Entropy | |
| |
| |
| |
| |
Empirical Risk Minimization | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Error Backpropagation Algorithm | |
| |
Error Correcting Output Codes | |
| |
Error-Ambiguity Decomposition | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Exponential Loss Function | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Feedforward Neural Network | |
| |
| |
| |
| |
| |
| |
Forward Stagewise Algorithm | |
Fractionally Strided Convolution | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Generalization Error Bound | |
| |
Generalized Lagrange Function | |
| |
Generalized Rayleigh Quotient | |
Generative Adversarial Network | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Gradient Exploding Problem |
|
| |
Graph Convolutional Network | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| |
| |
| |
Hyperparameter Optimization | |
| |
| |
| |
| |
| |
| |
Improved Iterative Scaling | |
| |
Independent and Identically Distributed |
|
| |
| |
| |
| |
| |
Inductive Logic Programming | |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
| |
| |
Joint Probability Distribution | |
| |
| |
Karush-Kuhn-Tucker Condition | |
| |
| |
| |
| |
Kernelized Linear Discriminant Analysis | |
| |
|
|
| |
| |
| |
| |
| |
| |
| |
Latent Dirichlet Allocation | |
| |
| |
| |
| |
| |
|
|
| |
| |
| |
| |
Learning Vector Quantization | |
| |
Least Squares Regression Tree | |
| |
| |
Linear Chain Conditional Random Field | |
Linear Classification Model | |
| |
| |
Linear Discriminant Analysis |
|
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
| |
Long Short-Term Memory Network | |
| |
| |
Low Rank Matrix Approximation | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Maximum Likelihood Estimation | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Minimal Description Length | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Multi-Class Classification | |
| |
Multi-Head Self-Attention | |
| |
| |
Multi-Layer Feedforward Neural Networks | |
| |
| |
Multiple Dimensional Scaling | |
Multiple Linear Regression | |
| |
Multivariate Normal Distribution | |
| |
| |
| |
| |
Nearest Neighbor Classifier | |
| |
Neighbourhood Component Analysis | |
| |
| |
| |
| |
| |
| |
Noise-Contrastive Estimation | |
| |
| |
| |
Non-Negative Matrix Factorization | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Parallel Distributed Processing | |
| |
| |
| |
| |
| |
| |
| |
Partially Observable Markov Decision Processes | |
| |
| |
| |
| |
| |
| |
| |
| |
Polynomial Kernel Function | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Principal Component Analysis | |
| |
Probabilistic Context-Free Grammar | |
Probabilistic Graphical Model | |
| |
Probability Density Function | |
| |
Probably Approximately Correct | |
| |
Prototype-Based Clustering | |
Proximal Gradient Descent | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Reproducing Kernel Hilbert Space | |
| |
| |
| |
| |
Restricted Boltzmann Machine | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Semi-Definite Programming | |
Semi-Naive Bayes Classifiers | |
Semi-Restricted Boltzmann Machine | |
Semi-Supervised Clustering | |
| |
Semi-Supervised Support Vector Machine | |
| |
| |
| |
| |
| |
| |
| |
| |
Simultaneous Localization And Mapping | |
| |
Singular Value Decomposition | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Standard Normal Distribution | |
| |
| |
State-Action Value Function | |
| |
| |
| |
| |
Stochastic Gradient Descent | |
| |
| |
| |
| |
| |
Structural Risk Minimization | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Temporal-Difference Learning | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Time Delay Neural Network | |
Time Homogenous Markov Chain | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
|
|
Transductive Transfer Learning | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Undirected Graphical Model | |
| |
|
|
| |
Universal Approximation Theorem | |
| |
Universal Function Approximator | |
| |
Unsupervised Layer-Wise Training | |
| |
| |
| |
| |
| |
| |
Value Function Approximation | |
|
|
Vanishing Gradient Problem | |
Vapnik-Chervonenkis Dimension | |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
| |
Weakly Supervised Learning | |
| |
| |
| |
| |
| |
| |
Within-Class Scatter Matrix | |
| |
Word Sense Disambiguation | |
| |
| |
| |
| |