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Paper Citation Record · LEDGER

Exploring Generative Networks for Manifolds with Non-Trivial Topology

As of 19 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 0 inbound Pith citation observations for arXiv:2502.02127.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2502.02127 v1

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measured 29 of 29 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-09T13:19:35.196312Z

measured 29 of 29 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

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29 of 29 outbound references displayed

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Outbound references

Observation 3a934520-a367-421d-a708-f592aa879f76 · outbound

This paper cites Wolff,CRITICAL SLOWING DOWN, Nucl.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Wolff,CRITICAL SLOWING DOWN, Nucl

Reference 1

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Observation 7730bd82-68f1-4e9b-90a7-c132dec74f05 · outbound

This paper cites Critical slowing down of topological modes.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Critical slowing down of topological modes

Reference 2

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Observation f80ce6bf-ae81-431e-b0f8-efa9d79d01c8 · outbound

This paper cites Critical slowing down and error analysis in lattice QCD simulations.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Critical slowing down and error analysis in lattice QCD simulations

Reference 3

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Observation 2deb9bbf-b130-43fb-941a-ed7aa9d324a8 · outbound

This paper cites Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Advances in machine-learning-based sampling motivated by lattice quantum chromodynamics

Reference 4

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Observation ae2b4fc3-5e45-4a22-ab78-e9e23dd84431 · outbound

This paper cites Flow-based sampling for lattice field theories.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Flow-based sampling for lattice field theories

Reference 5

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Observation 1df343c0-7850-4117-8c3c-fdaae0453484 · outbound

This paper cites Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Physics-Driven Learning for Inverse Problems in Quantum Chromodynamics

Reference 6

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Observation 1652f704-c4b4-43e8-a11a-a5b185dc69ad · outbound

This paper cites Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Reducing Autocorrelation Times in Lattice Simulations with Generative Adversarial Networks

Reference 7

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Observation 6b9bdedb-1391-4e0b-a903-87c3473bf082 · outbound

This paper cites Continuous-mixture Autoregressive Networks for efficient variational calculation of many-body systems.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Continuous-mixture Autoregressive Networks for efficient variational calculation of many-body systems

Reference 8

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Observation 02788ed1-bf40-40cf-89c9-25f41860b379 · outbound

This paper cites Diffusion Models as Stochastic Quantization in Lattice Field Theory.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Diffusion Models as Stochastic Quantization in Lattice Field Theory

Reference 9

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Observation 81ad0fae-3f81-42d6-8d14-f7c03e3ae1eb · outbound

This paper cites Generative Diffusion Models for Lattice Field Theory.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Generative Diffusion Models for Lattice Field Theory

Reference 10

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Observation ebfc961f-7311-4e5d-8ab3-fd3df6048430 · outbound

This paper cites Diffusion models for lattice gauge field simulations.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Diffusion models for lattice gauge field simulations

Reference 11

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Observation f5d1b838-37af-4cf4-a48e-6e38e06ec512 · outbound

This paper cites On learning higher-order cumulants in diffusion models.

Exploring Generative Networks for Manifolds with Non-Trivial Topology On learning higher-order cumulants in diffusion models

Reference 12

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Observation 3430d96d-32ef-432d-9dd2-8eda17c0fd4e · outbound

This paper cites Variational Inference with Normalizing Flows.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Variational Inference with Normalizing Flows

Reference 13

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Observation 65d363f8-df24-4684-a354-0b36d2ab9a53 · outbound

This paper cites Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning

Reference 14

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Observation 199bf67d-a33d-4d21-8e67-2b3d1edda4ac · outbound

This paper cites Flow-based generative models for Markov chain Monte Carlo in lattice field theory.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Flow-based generative models for Markov chain Monte Carlo in lattice field theory

Reference 15

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Observation c4103eac-99bc-4c93-b0af-da9ec7ab07c0 · outbound

This paper cites Asymptotically unbiased estimation of physical observables with neural samplers.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Asymptotically unbiased estimation of physical observables with neural samplers

Reference 16

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Observation 8c156928-7fc4-4e02-9993-462c728e25a9 · outbound

This paper cites Equivariant flow-based sampling for lattice gauge theory.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Equivariant flow-based sampling for lattice gauge theory

Reference 17

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Observation 3e0ba22e-de06-4639-9446-d1c5570eea96 · outbound

This paper cites Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models

Reference 18

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Observation 540d156d-5c20-44f0-ad04-11079d5c58fd · outbound

This paper cites Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Detecting and Mitigating Mode-Collapse for Flow-based Sampling of Lattice Field Theories

Reference 19

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Observation f4c32e49-f9be-4ba4-990a-fb1267163f36 · outbound

This paper cites Neural Ordinary Differential Equations.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Neural Ordinary Differential Equations

Reference 20

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Observation 5f2527be-1eb3-40da-8286-c4c0ed0c1cca · outbound

This paper cites Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Scaling Up Machine Learning For Quantum Field Theory with Equivariant Continuous Flows

Reference 21

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Observation 3a1797f6-c5c4-4b40-93a9-61d1539a9a64 · outbound

This paper cites Learning Lattice Quantum Field Theories with Equivariant Continuous Flows.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Learning Lattice Quantum Field Theories with Equivariant Continuous Flows

Reference 22

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Observation ed9cceab-2f2d-43d9-ba8f-80759a31818a · outbound

This paper cites Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Sampling the lattice Nambu-Goto string using Continuous Normalizing Flows

Reference 23

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Observation 32d2dd6c-f826-4161-947b-cad1fff9b7d2 · outbound

This paper cites Stochastic Normalizing Flows.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Stochastic Normalizing Flows

Reference 24

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Observation 1de6ec55-d83d-479e-8e74-c2d55ebed90e · outbound

This paper cites Stochastic normalizing flows as non-equilibrium transformations.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Stochastic normalizing flows as non-equilibrium transformations

Reference 25

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Observation 28356d38-daa7-4946-b9d8-d72bd25b11f0 · outbound

This paper cites Numerical determination of the width and shape of the effective string using Stochastic Normalizing Flows.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Numerical determination of the width and shape of the effective string using Stochastic Normalizing Flows

Reference 26

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Observation 3c38f39b-5531-41aa-aa07-2024313cb446 · outbound

This paper cites Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

Reference 27

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Observation e7d3312e-0ce9-43d1-89a6-bc28a816cdc8 · outbound

This paper cites Bengio, T.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Bengio, T

Reference 28

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Observation 37c98f51-647a-49ee-b6c9-b2f0ab1fb3d3 · outbound

This paper cites Introduction to Normalizing Flows for Lattice Field Theory.

Exploring Generative Networks for Manifolds with Non-Trivial Topology Introduction to Normalizing Flows for Lattice Field Theory

Reference 29

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