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Flow-based generative model

A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.

Products, Flows on manifolds & Overview

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Flows on manifolds

Smooth manifold PDFs Riemann manifolds Isometrically Angular spherical coordinates

18 related topics

Overview

Generative Machine learning Probability distribution Change-of-variable Loss function

22 related topics

Variants

Stochastic process Reparameterization trick Rademacher distribution Neural ODE Homeomorphism

10 related topics

Method

Random variable Inverse function Derivation Deep neural networks Determinant

9 related topics

Topics to explore

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Overview

Generative Generative model
Machine learning
Probability distribution
Change-of-variable Probability density function
Loss function
Variational autoencoders (VAEs) Variational autoencoder
Generative adversarial networks (GANs) Generative adversarial network
Diffusion models Diffusion model
Likelihood function
Dirichlet Dirichlet distribution
Softmax function
SGB distribution
Discriminative models Discriminative model
Temperature scaling Platt scaling
Eigenvector
Positive definite Positive definite matrix
Group Group mathematics
Linear algebra
Automatic differentiation
Quadratic equation
ACG distribution
Matrix determinant lemma

Method

Random variable
Inverse function
Derivation Flow-based generative model
Deep neural networks Deep learning
Determinant
Jacobian matrix Jacobian matrix and determinant
Inverse function theorem
Invertible matrix
Induction Mathematical induction

Training method

Kullback–Leibler divergence
Importance sampling
Maximizing the model likelihood Maximum likelihood estimation
Gradient descent

Variants

Stochastic process
Reparameterization trick
Rademacher distribution
Neural ODE
Homeomorphism
Ambient isotopy
Turn a sphere inside out Sphere eversion
Whitney embedding theorem
Universal approximation theorem
Optimal transport theory Optimal transport

Flows on manifolds

Smooth manifold
PDFs Probability density
Riemann manifolds Riemannian manifold
Isometrically Isometry
Angular spherical coordinates N-sphere
Von Mises-Fisher Von Mises-Fisher distribution
Simplex
Categorical distributions Categorical distribution
Bijection
Diffeomorphism
As explained here Projected normal distribution
Lebesgue measure
Tangent space
Parallelotope Parallelepiped
Gram determinant
Chain rule for function composition Chain rule
Idempotent linear projection Projection (linear algebra)
Left inverse Left inverse function

Downsides

Typical set Typical Set?action=edit&redlink=1

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Map overview Semantic statistics

Flow-based generative model

Nodes71

Edges70

Triples3

Avg. degree1.97

Density0.028169

Components1

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Flow-based generative model

Top relations

has application · 2

Flow-based generative model → Audio, Flow-based

is a · 1

Flow-based generative model → generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow

Important terminology Word statistics

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Important terminology

displaystyle mathbf flow jacobian mathbb text function n-1 volume distribution transform differential inverse -1 model ratio flows matrix space theta

Entity relationships Subject–Predicate–Object triples

Subject	Predicate	Object	Confidence	Src
Flow-based generative model	is a	generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow	0.90	text
Flow-based generative model	has application	Flow-based	0.60	section
Flow-based generative model	has application	Audio	0.60	section

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Min side: 3

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