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Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:2506.09679.

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pith.paper-citation-record.v1
2506.09679 v4

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57 of 57 outbound references displayed

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Outbound references

Observation db572b7c-d429-4fa0-b537-5b4fc719de7b · outbound

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

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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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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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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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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

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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

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

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

Reference 32

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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

Reference 34

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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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Observation fe84aa8f-b6a9-44c1-847e-063a3dd0d846 · outbound

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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Observation 8c774361-0eee-4f2b-bd23-699365455822 · outbound

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

Reference 37

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Observation 03491537-beef-4237-872e-3bcd05f2b6a3 · outbound

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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source=pdf_text observed=2026-08-07T04:50:29.477968Z digest=sha256:07220d577c8ba82ee1d99e3de294fbfc90fd53e3a2bbd9afeb7c0f7f80ffb0ad

Observation db2dadca-6d25-4489-a71c-e233f7304678 · outbound

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

Reference 39

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Observation 3b2c0218-d59d-44d8-96cf-738ebc67466f · outbound

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

Reference 40

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raw_fallback, observed 2026-08-07T04:50:29.818531Z

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Observation d724e15d-f1fc-4b55-b63a-851c8df94504 · outbound

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

Reference 41

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Observation cf468453-606c-4933-a0c8-4d5b59b81e08 · outbound

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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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation bce84c2b-90bd-4a2e-9a06-d2c941841974 · outbound

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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raw_fallback, observed 2026-08-07T04:50:30.277509Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation e42ebb34-bcf3-4252-b25a-e406fe4f988e · outbound

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

Reference 44

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local_arxiv, observed 2026-08-07T04:50:29.708632Z

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Observation a9b3a07f-cd8c-45a5-a906-de97a74a2dcd · outbound

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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source=pdf_text observed=2026-08-07T04:50:29.499501Z digest=sha256:174af82961e87cf2b40035a40176a07b1ebe27cd02b4b7841467c07a52e47812

Observation c8a90f2b-5ddc-472b-b7d9-af42f91abd52 · outbound

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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verified fuzzy
raw_fallback, observed 2026-08-07T04:50:30.268092Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T04:50:29.502563Z digest=sha256:6db0ae696ec4c630d140d9dfd3d32a039498eb13a06e367fbd54399602a01c65

Observation 6438a7ca-9bee-41f3-893d-f92a633f54f7 · outbound

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

Reference 47

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Observation e677b092-1fc0-473d-8a94-dc261b05bbc2 · outbound

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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source=pdf_text observed=2026-08-07T04:50:29.508093Z digest=sha256:5ce4957239f5563a4aba9946ded9691e9a020898f01b4bb67e287837d1a3ff7d

Observation f314be58-02c5-4e74-bc2c-d4e2d7e17dc3 · outbound

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

Reference 49

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source=pdf_text observed=2026-08-07T04:50:29.510881Z digest=sha256:89928b418561f7987511a88a710ca2693c76cdb48cff634131e8ebc430882bc8

Observation 4a19dfb1-91f1-4be4-b0ae-64094a476140 · outbound

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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raw_fallback, observed 2026-08-07T04:50:30.259151Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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Observation 3134ddd6-bf09-4b08-9c87-c524beff247e · outbound

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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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T04:50:29.517066Z digest=sha256:97850df05f70466c1c00542f6a3d792b1ae414bd24e8d3ac8c78c6b204096fb6

Observation 3b080c99-da99-4228-8b6a-d9cdc3a5e47c · outbound

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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raw_fallback, observed 2026-08-07T04:50:30.250197Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T04:50:29.519994Z digest=sha256:80db5b2d8bcebd87b74b45062ebea049eda3490042b2cfd7f4a80bd3befbe0d6

Observation cd59847e-334b-4423-832d-4c2fdb74fd3e · outbound

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

Reference 2017

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local_arxiv, observed 2026-08-07T04:50:29.724951Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T04:50:29.491655Z digest=sha256:fccb0730e544de190c322c6af90e92fc5ed0771a4f268257cb4ce092d8eb7ccd

Observation c5fb0337-0fa4-41fe-b9a2-662a7fa851fd · outbound

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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source=pdf_text observed=2026-08-07T04:50:29.394261Z digest=sha256:97bfeac9c8541ba3d00ae9e95c06261185036164cbbab4d0e420595571fee42d

Observation 22324e29-1541-45a4-998f-9feade89def3 · outbound

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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Observation aa4cb1b2-a869-4a3b-a5e9-853c4d4c9e30 · outbound

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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local_arxiv, observed 2026-08-07T04:50:29.903854Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T04:50:29.456196Z digest=sha256:159e5afc46f0274ccd8ec08eafedc7559b7180eb0541a9b4562563ec1ba63913

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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verified exact
local_arxiv, observed 2026-08-07T04:50:30.085929Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-07T04:50:29.414607Z digest=sha256:ba407c16f24fa8897ec6e19ba3a932ce4630248d4fcd96d23630ff93b2c7228b

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