Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold | Paper Citation Record

Observation 30867ddd-18aa-4cf7-907d-01183c6b036b · outbound

This paper cites Natural gradient works efficiently in learning.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Natural gradient works efficiently in learning

Reference 1

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This paper cites Methods of information geometry, volume 191.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Methods of information geometry, volume 191

Reference 2

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This paper cites Gradient flows: in metric spaces and in the space of probability measures.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Gradient flows: in metric spaces and in the space of probability measures

Reference 3

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Observation 90fe56dd-015a-4b2d-8e13-06911ff57ce4 · outbound

This paper cites Kernelized wasserstein natural gradient.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Kernelized wasserstein natural gradient

Reference 4

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This paper cites Kernel conditional exponential family.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Kernel conditional exponential family

Reference 5

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This paper cites Maximum mean discrepancy gradient flow.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Maximum mean discrepancy gradient flow

Reference 6

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This paper cites Wasserstein generative adversarial networks.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Wasserstein generative adversarial networks

Reference 7

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This paper cites Exact natural gradient in deep linear networks and its application to the nonlinear case.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Exact natural gradient in deep linear networks and its application to the nonlinear case

Reference 8

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Observation 7da3c650-3d7b-47f1-8418-97653f6c0e06 · outbound

This paper cites Diffusion schr\"odinger bridge with applications to score-based generative modeling.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Diffusion schr\"odinger bridge with applications to score-based generative modeling

Reference 9

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This paper cites Statistical inference, 2nd Edition.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Statistical inference, 2nd Edition

Reference 10

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This paper cites Neural ordinary differential equations.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Neural ordinary differential equations

Reference 11

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Observation 4a2d5e42-01b6-49bd-9388-00f32afe1045 · outbound

This paper cites Rethinking the diffusion models for missing data imputation: A gradient flow perspective.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Rethinking the diffusion models for missing data imputation: A gradient flow perspective

Reference 12

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Observation c0b7169f-2781-420b-bb66-6fa1894961a2 · outbound

This paper cites Teng: Time-evolving natural gradient for solving pdes with deep neural nets toward machine precision.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Teng: Time-evolving natural gradient for solving pdes with deep neural nets toward machine precision

Reference 13

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Observation d7005b89-fd8e-4323-a544-4a0f2e8a8462 · outbound

This paper cites Underdamped langevin mcmc: A non-asymptotic analysis.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Underdamped langevin mcmc: A non-asymptotic analysis

Reference 14

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This paper cites Log-Concave Sampling.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Log-Concave Sampling

Reference 15

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This paper cites Svgd as a kernelized wasserstein gradient flow of the chi-squared divergence.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Svgd as a kernelized wasserstein gradient flow of the chi-squared divergence

Reference 16

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Observation ebde1dd0-b13a-4ffe-929b-a17229ae31f0 · outbound

This paper cites Scalable wasserstein gradient flow for generative modeling through unbalanced optimal transport.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Scalable wasserstein gradient flow for generative modeling through unbalanced optimal transport

Reference 17

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This paper cites Density ratio estimation via infinitesimal classification.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Density ratio estimation via infinitesimal classification

Reference 18

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This paper cites Optimal transport for domain adaptation.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Optimal transport for domain adaptation

Reference 19

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This paper cites Sparse inverse covariance estimation with the graphical lasso.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Sparse inverse covariance estimation with the graphical lasso

Reference 20

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Observation 64ad0af0-5904-4a34-9780-6812dbb58cff · outbound

This paper cites Deep generative learning via variational gradient flow.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Deep generative learning via variational gradient flow

Reference 21

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Observation d419a101-b828-40e9-a502-8ed8dbb49da4 · outbound

This paper cites Fast approximate natural gradient descent in a kronecker factored eigenbasis.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Fast approximate natural gradient descent in a kronecker factored eigenbasis

Reference 22

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This paper cites Geodesic flow kernel for unsupervised domain adaptation.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Geodesic flow kernel for unsupervised domain adaptation

Reference 23

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Observation 31b2536a-deff-4cd3-82cf-3398a6f97c4c · outbound

This paper cites Generative adversarial nets.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Generative adversarial nets

Reference 24

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Observation b3e50466-8b12-45ca-bc10-10e11061d799 · outbound

This paper cites Borgwardt, Malte J.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Borgwardt, Malte J

Reference 25

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This paper cites A kronecker-factored approximate fisher matrix for convolution layers.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold A kronecker-factored approximate fisher matrix for convolution layers

Reference 26

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Observation b892cf9e-e2f4-4f1b-b15f-ccff7190e175 · outbound

This paper cites Vector quantized diffusion model for text-to-image synthesis.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Vector quantized diffusion model for text-to-image synthesis

Reference 27

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Observation 8ec5d3f0-d305-4712-aa9e-7eefdb7cb6f1 · outbound

This paper cites Posterior sampling based on gradient flows of the MMD with negative distance kernel.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Posterior sampling based on gradient flows of the MMD with negative distance kernel

Reference 28

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Observation ea27392d-844f-4fe3-9bb7-f023d79f67b2 · outbound

This paper cites Training Neural Samplers with Reverse Diffusive KL Divergence.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Training Neural Samplers with Reverse Diffusive KL Divergence

Reference 29

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This paper cites Denoising diffusion probabilistic models.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Denoising diffusion probabilistic models

Reference 30

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Observation 353b2042-5e1a-49d0-9858-0f5e5677dbc7 · outbound

This paper cites Neural tangent kernel: convergence and generalization in neural networks.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Neural tangent kernel: convergence and generalization in neural networks

Reference 31

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This paper cites Kingma and Max Welling.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Kingma and Max Welling

Reference 32

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This paper cites Neural speech synthesis with transformer network.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Neural speech synthesis with transformer network

Reference 33

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Observation 6ffa20dd-7145-4476-a432-7d92655c24d7 · outbound

This paper cites Affine natural proximal learning.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Affine natural proximal learning

Reference 34

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Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Unresolved cited work

Reference 35

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Observation 54000824-b837-465b-aea1-484fbce58d38 · outbound

This paper cites Stein variational gradient descent as gradient flow.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Stein variational gradient descent as gradient flow

Reference 36

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This paper cites Minimizing f -divergences by interpolating velocity fields.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Minimizing f -divergences by interpolating velocity fields

Reference 37

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This paper cites Flow straight and fast: Learning to generate and transfer data with rectified flow.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Flow straight and fast: Learning to generate and transfer data with rectified flow

Reference 38

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Observation 45ba2a97-91a8-44c7-ba24-251147266e2c · outbound

This paper cites Optimizing neural networks with kronecker-factored approximate curvature.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Optimizing neural networks with kronecker-factored approximate curvature

Reference 39

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This paper cites Sampling in unit time with kernel fisher-rao flow.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Sampling in unit time with kernel fisher-rao flow

Reference 40

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This paper cites Alemi, Jascha Sohl-Dickstein, and Samuel S.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Alemi, Jascha Sohl-Dickstein, and Samuel S

Reference 41

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Observation 99bfe56d-c7ac-4d30-bca7-ad6087faad9c · outbound

This paper cites Dataset shift in machine learning.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Dataset shift in machine learning

Reference 42

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This paper cites Variational inference with normalizing flows.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Variational inference with normalizing flows

Reference 43

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This paper cites High-resolution image synthesis with latent diffusion models.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold High-resolution image synthesis with latent diffusion models

Reference 44

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Observation e5b3a66e-b2da-4a80-aca5-83e3a841fbbe · outbound

This paper cites How to Train Your Energy-Based Models.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold How to Train Your Energy-Based Models

Reference 45

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Observation 513a7996-b39c-41c0-8b5a-d24c0b12b588 · outbound

This paper cites Score-based generative modeling through stochastic differential equations.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Score-based generative modeling through stochastic differential equations

Reference 46

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Observation 7c7213e7-496d-4bea-8230-6f6e5896b46d · outbound

This paper cites Density estimation in infinite dimensional exponential families.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Density estimation in infinite dimensional exponential families

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:10:07.926223Z

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Observation 3a742811-36bb-4d3c-8b14-8aa29337496c · outbound

This paper cites Direct importance estimation with model selection and its application to covariate shift adaptation.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Direct importance estimation with model selection and its application to covariate shift adaptation

Reference 48

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Observation 1aa7c45f-e666-4f31-93f8-acebf799769b · outbound

This paper cites Naturalspeech: End-to-end text-to-speech synthesis with human-level quality.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Naturalspeech: End-to-end text-to-speech synthesis with human-level quality

Reference 49

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This paper cites Graphical models, exponential families, and variational inference.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Graphical models, exponential families, and variational inference

Reference 50

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Observation 0c021102-a2f4-418b-8a60-12d1d1ad2eb8 · outbound

This paper cites Diffusion-gan: Training gans with diffusion.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Diffusion-gan: Training gans with diffusion

Reference 51

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This paper cites Kernel ridge regression.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Kernel ridge regression

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:10:07.859371Z

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Observation 7778fa59-66b1-4661-bdb9-7b26f2dfb7bc · outbound

This paper cites Sampling as optimization in the space of measures: The langevin dynamics as a composite optimization problem.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold Sampling as optimization in the space of measures: The langevin dynamics as a composite optimization problem

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:10:07.843825Z

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Observation f0911292-7b7b-4d46-8537-d0aad79813f4 · outbound

This paper cites High-dimensional differential parameter inference in exponential family using time score matching.

Guiding Time-Varying Generative Models with Natural Gradients on Exponential Family Manifold High-dimensional differential parameter inference in exponential family using time score matching

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