Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature | Paper Citation Record

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This paper cites the gauss and codazzi equations.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature the gauss and codazzi equations

Reference 1

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This paper cites Geometrically Enriched Latent Spaces.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Geometrically Enriched Latent Spaces

Reference 2

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This paper cites Latent Space Oddity: on the Curvature of Deep Generative Models.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Latent Space Oddity: on the Curvature of Deep Generative Models

Reference 3

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This paper cites Restricted Strong Convexity of Deep Learning Models with Smooth Activations.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Restricted Strong Convexity of Deep Learning Models with Smooth Activations

Reference 4

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This paper cites Manifold regularization: A geometric framework for learning from labeled and unlabeled examples.Journal of Machine Learning Research, 7(85):2399–2434, 2006.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Manifold regularization: A geometric framework for learning from labeled and unlabeled examples.Journal of Machine Learning Research, 7(85):2399–2434, 2006

Reference 5

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This paper cites Chapter 7: Ricci flow, 2023.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Chapter 7: Ricci flow, 2023

Reference 6

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This paper cites Variational Autoencoders: A Harmonic Perspective.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Variational Autoencoders: A Harmonic Perspective

Reference 7

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This paper cites A Geometric Perspective on Variational Autoencoders.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature A Geometric Perspective on Variational Autoencoders

Reference 8

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This paper cites Geometry-aware hamiltonian variational auto-encoder, 2020.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Geometry-aware hamiltonian variational auto-encoder, 2020

Reference 9

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This paper cites Learning Flat Latent Manifolds with VAEs.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Learning Flat Latent Manifolds with VAEs

Reference 10

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This paper cites Chervon, F.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Chervon, F

Reference 11

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This paper cites Springer International Publishing,.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Springer International Publishing,

Reference 12

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Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Unresolved cited work

Reference 13

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Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Unresolved cited work

Reference 14

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This paper cites do Carmo.Riemannian Geometry.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature do Carmo.Riemannian Geometry

Reference 15

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This paper cites The christoffel symbols with a diagonal metric, 2019.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature The christoffel symbols with a diagonal metric, 2019

Reference 16

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This paper cites Dynamical variational autoencoders: A comprehensive review.Foundations and Trends® in Machine Learning, 15(1–2):1–175, 2021.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Dynamical variational autoencoders: A comprehensive review.Foundations and Trends® in Machine Learning, 15(1–2):1–175, 2021

Reference 17

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This paper cites Variational autoencoders with latent high-dimensional steady geometric flows for dynamics,.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Variational autoencoders with latent high-dimensional steady geometric flows for dynamics,

Reference 18

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This paper cites Hamilton.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Hamilton

Reference 19

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Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Unresolved cited work

Reference 20

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This paper cites Robust Learning with Jacobian Regularization.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Robust Learning with Jacobian Regularization

Reference 21

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Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Unresolved cited work

Reference 22

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Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Unresolved cited work

Reference 23

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This paper cites Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Rate-Distortion Optimization Guided Autoencoder for Isometric Embedding in Euclidean Latent Space

Reference 24

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This paper cites Adversarial robustness of VAEs through the lens of local geometry.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Adversarial robustness of VAEs through the lens of local geometry

Reference 25

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This paper cites Kim, Nicolas Perrin-Gilbert, Erkan Narmanli, Paul Klein, Christopher R.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Kim, Nicolas Perrin-Gilbert, Erkan Narmanli, Paul Klein, Christopher R

Reference 26

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This paper cites Kingma and Max Welling.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Kingma and Max Welling

Reference 27

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This paper cites Auto-Encoding Variational Bayes.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Auto-Encoding Variational Bayes

Reference 28

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This paper cites On the direct alignment of latent spaces.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature On the direct alignment of latent spaces

Reference 29

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This paper cites Metric regularization of latent spaces via ricci-type flows.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Metric regularization of latent spaces via ricci-type flows

Reference 30

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This paper cites On explicit curvature regularization in deep generative models,.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature On explicit curvature regularization in deep generative models,

Reference 31

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This paper cites Atzberger.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Atzberger

Reference 32

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This paper cites GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions

Reference 33

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This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature Machine Intelligence, 3(3):218–229, March 2021.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.Nature Machine Intelligence, 3(3):218–229, March 2021

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This paper cites Millman and George D.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Millman and George D

Reference 35

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This paper cites Robustness via curvature regularization, and vice versa.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Robustness via curvature regularization, and vice versa

Reference 36

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This paper cites A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning

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This paper cites Poincar\'e Embeddings for Learning Hierarchical Representations.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Poincar\'e Embeddings for Learning Hierarchical Representations

Reference 38

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This paper cites The entropy formula for the Ricci flow and its geometric applications.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature The entropy formula for the Ricci flow and its geometric applications

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This paper cites Adversarial robustness via fisher-rao regularization.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3):2698–2710, March 2023.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Adversarial robustness via fisher-rao regularization.IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(3):2698–2710, March 2023

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This paper cites Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations

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This paper cites Thomas Fletcher.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Thomas Fletcher

Reference 42

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This paper cites Hamilton’s ricci flow, 2006.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Hamilton’s ricci flow, 2006

Reference 43

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This paper cites Geometry of Deep Generative Models for Disentangled Representations.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Geometry of Deep Generative Models for Disentangled Representations

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This paper cites Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds

Reference 45

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This paper cites Lectures on the ricci flow, 2006.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Lectures on the ricci flow, 2006

Reference 46

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This paper cites Adversarial attacks on neural networks through canonical riemannian foliations.Machine Learning, 113(11-12):8655–8686, October 2024.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Adversarial attacks on neural networks through canonical riemannian foliations.Machine Learning, 113(11-12):8655–8686, October 2024

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This paper cites Understanding and mitigating gradient pathologies in physics-informed neural networks.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Understanding and mitigating gradient pathologies in physics-informed neural networks

Reference 48

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This paper cites An Expert's Guide to Training Physics-informed Neural Networks.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature An Expert's Guide to Training Physics-informed Neural Networks

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This paper cites Weisstein.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Weisstein

Reference 50

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This paper cites The relationship between ricci and gaussian curvatures.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature The relationship between ricci and gaussian curvatures

Reference 51

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This paper cites gjj ∂j(−Γij kRickk −Γ kj iRicii) # (28) + X j gjj X m∈{j,i,k}all distinct.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature gjj ∂j(−Γij kRickk −Γ kj iRicii) # (28) + X j gjj X m∈{j,i,k}all distinct

Reference 52

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This paper cites The Riemannian Geometry of Deep Generative Models.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature The Riemannian Geometry of Deep Generative Models

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This paper cites doi: 10.1007/978-3-030-58580-8_2.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature doi: 10.1007/978-3-030-58580-8_2

Reference 2020

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This paper cites Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems

Reference 2021

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This paper cites On Explicit Curvature Regularization in Deep Generative Models.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature On Explicit Curvature Regularization in Deep Generative Models

Reference 2023

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Observation 854f2f02-9e22-4c14-88ef-af1c02fd1c7e · outbound

This paper cites Variational autoencoders with latent high-dimensional steady geometric flows for dynamics.

Geometric flow regularization in latent spaces for smooth dynamics with the efficient variations of curvature Variational autoencoders with latent high-dimensional steady geometric flows for dynamics

Reference 2025

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