Paper Citation Record · LEDGER

Provable Maximum Entropy Manifold Exploration via Diffusion Models

As of 17 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 1 inbound Pith citation observation for arXiv:2506.15385.

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

pith.paper-citation-record.v1
2506.15385 v1

Coverage vector

measured 73 of 73 reference resolution

Typed states for the displayed outbound observations.

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measured 74 of 74 standing notices

One-hop event checks from named stored sources.

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T20:49:46.204608Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-20T20:53:43.719842Z

Reference resolution

73 of 73 outbound references displayed

verified exact2
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6f992545-acea-4d4e-a929-de7246df8df6 · outbound

This paper cites write newline.

Provable Maximum Entropy Manifold Exploration via Diffusion Models write newline

Reference 1

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Observation 98bc49f1-9361-43ed-9cd9-cdc61316a9eb · outbound

This paper cites Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning

Reference 2

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Observation 4f21f19b-fd16-495b-b6a7-3e9c04ec3dd9 · outbound

This paper cites Mirror descent with relative smoothness in measure spaces, with application to sinkhorn and em.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Mirror descent with relative smoothness in measure spaces, with application to sinkhorn and em

Reference 3

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Observation e70237ad-35a8-4140-b145-ee0357079f45 · outbound

This paper cites Dynamics of stochastic approximation algorithms.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Dynamics of stochastic approximation algorithms

Reference 4

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Observation 81bf3f2f-579c-4e87-8940-5c4984835e12 · outbound

This paper cites and Hirsch, M.

Provable Maximum Entropy Manifold Exploration via Diffusion Models and Hirsch, M

Reference 5

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Observation 205e1bf3-1e22-48cd-b1a0-068a9b6874c3 · outbound

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Unresolved cited work

Reference 6

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Observation 00519aa2-131a-41b2-9901-b5b9b28e4382 · outbound

This paper cites Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data

Reference 7

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Observation a271f04b-3cfc-417f-8e7a-b91a53377195 · outbound

This paper cites Diffusion Policy: Visuomotor Policy Learning via Action Diffusion.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Diffusion Policy: Visuomotor Policy Learning via Action Diffusion

Reference 8

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Observation 9f54d88b-85ff-463a-b3a5-72feacdfd1b4 · outbound

This paper cites DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking.

Provable Maximum Entropy Manifold Exploration via Diffusion Models DiffDock: Diffusion Steps, Twists, and Turns for Molecular Docking

Reference 9

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Observation 6e4ba135-adf8-4f63-99b1-9064f87caafd · outbound

This paper cites Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models

Reference 10

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Observation a2631428-ff86-4f35-b464-27b11adf6e58 · outbound

This paper cites and Madre, J.

Provable Maximum Entropy Manifold Exploration via Diffusion Models and Madre, J

Reference 11

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Observation 67cee07c-8108-4a82-897b-37b9949bb4fa · outbound

This paper cites Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Geometric Active Exploration in Markov Decision Processes: the Benefit of Abstraction

Reference 12

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Observation 43bb994f-5265-404a-a509-4413bb4ae130 · outbound

This paper cites Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Global Reinforcement Learning: Beyond Linear and Convex Rewards via Submodular Semi-gradient Methods

Reference 13

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Observation 981f37c7-6a63-4059-9f13-8e5e1ae75a1c · outbound

This paper cites Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Adjoint Matching: Fine-tuning Flow and Diffusion Generative Models with Memoryless Stochastic Optimal Control

Reference 14

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Observation e4d8c78e-70a3-4f0d-8a32-9d8e21701dbd · outbound

This paper cites Reinforcement learning in continuous time and space.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Reinforcement learning in continuous time and space

Reference 15

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Observation c1181c10-3ee5-404a-b1bd-c3249cf0c4f4 · outbound

This paper cites and Zhu, J.-J.

Provable Maximum Entropy Manifold Exploration via Diffusion Models and Zhu, J.-J

Reference 16

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Observation 50b76179-1b64-4f8b-a843-7708b2cc7a95 · outbound

This paper cites and Schuffenhauer, A.

Provable Maximum Entropy Manifold Exploration via Diffusion Models and Schuffenhauer, A

Reference 17

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Observation f3284c9e-fb2c-4739-a750-564836856021 · outbound

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Unresolved cited work

Reference 18

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Observation 688c10ce-958d-4da8-ab34-118057df8fb4 · outbound

This paper cites The Vendi Score: A Diversity Evaluation Metric for Machine Learning.

Provable Maximum Entropy Manifold Exploration via Diffusion Models The Vendi Score: A Diversity Evaluation Metric for Machine Learning

Reference 19

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Observation 019b40f2-f26b-43ec-b446-98ac3be08762 · outbound

This paper cites Geometric Entropic Exploration.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Geometric Entropic Exploration

Reference 20

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Unresolved cited work

Reference 21

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Observation ec0b5de5-a8b8-4e5b-b04f-d2b4f430373b · outbound

This paper cites Provably efficient maximum entropy exploration.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Provably efficient maximum entropy exploration

Reference 22

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Observation 7eff728a-ee9a-4f66-9c82-f12d243d0db9 · outbound

This paper cites Clipscore: A reference-free evaluation metric for image captioning.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Clipscore: A reference-free evaluation metric for image captioning

Reference 23

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Observation 1e5879b4-75f4-419f-a954-8f1028a265c7 · outbound

This paper cites Gans trained by a two time-scale update rule converge to a local nash equilibrium.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Gans trained by a two time-scale update rule converge to a local nash equilibrium

Reference 24

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Provable Maximum Entropy Manifold Exploration via Diffusion Models and Lemar \'e chal, C

Reference 25

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Observation bd6c75f7-6d69-4ef4-b95f-b7435eb1a2e0 · outbound

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Classifier-Free Diffusion Guidance

Reference 26

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Denoising diffusion probabilistic models

Reference 27

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This paper cites G., Vignac, C., and Welling, M.

Provable Maximum Entropy Manifold Exploration via Diffusion Models G., Vignac, C., and Welling, M

Reference 28

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Finding mixed nash equilibria of generative adversarial networks

Reference 29

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This paper cites The limits of min-max optimization algorithms: Convergence to spurious non-critical sets.

Provable Maximum Entropy Manifold Exploration via Diffusion Models The limits of min-max optimization algorithms: Convergence to spurious non-critical sets

Reference 30

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Provable Maximum Entropy Manifold Exploration via Diffusion Models and Zhou, X

Reference 31

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Provable Maximum Entropy Manifold Exploration via Diffusion Models A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models

Reference 32

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Provable Maximum Entropy Manifold Exploration via Diffusion Models R., Hsieh, Y.-P., and Krause, A

Reference 33

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Variational diffusion models

Reference 34

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This paper cites Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Shielded Diffusion: Generating Novel and Diverse Images using Sparse Repellency

Reference 35

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Convergence for score-based generative modeling with polynomial complexity

Reference 36

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This paper cites Efficient Exploration via State Marginal Matching.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Efficient Exploration via State Marginal Matching

Reference 37

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Provable Maximum Entropy Manifold Exploration via Diffusion Models A gradient descent perspective on sinkhorn

Reference 38

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Diffusion Model for Data-Driven Black-Box Optimization

Reference 39

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Information directed reward learning for reinforcement learning

Reference 40

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Provable Maximum Entropy Manifold Exploration via Diffusion Models and Abbeel, P

Reference 41

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Provable Maximum Entropy Manifold Exploration via Diffusion Models M., and Nesterov, Y

Reference 42

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Provable Maximum Entropy Manifold Exploration via Diffusion Models A unified stochastic approximation framework for learning in games

Reference 43

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Training diffusion models towards diverse image generation with reinforcement learning

Reference 44

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Active exploration via experiment design in M arkov chains

Reference 45

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Task-agnostic exploration via policy gradient of a non-parametric state entropy estimate

Reference 46

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Challenging common assumptions in convex reinforcement learning

Reference 47

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Provable Maximum Entropy Manifold Exploration via Diffusion Models The importance of non-markovianity in maximum state entropy exploration

Reference 48

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Convex reinforcement learning in finite trials

Reference 49

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Unresolved cited work

Reference 50

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Score-based generative models detect manifolds

Reference 51

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Submodular Reinforcement Learning

Reference 52

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Provable Maximum Entropy Manifold Exploration via Diffusion Models High-resolution image synthesis with latent diffusion models, 2021

Reference 53

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Provable Maximum Entropy Manifold Exploration via Diffusion Models CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling

Reference 54

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This paper cites Laion-5b: An open large-scale dataset for training next generation image-text models.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Laion-5b: An open large-scale dataset for training next generation image-text models

Reference 55

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Observation 36003c65-92d7-4a6d-9aea-d34e8136cf24 · outbound

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Provable Maximum Entropy Manifold Exploration via Diffusion Models State entropy maximization with random encoders for efficient exploration

Reference 56

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Observation 8afcec23-807e-4806-8bf6-d6d53d49e7ec · outbound

This paper cites The Superposition of Diffusion Models Using the It\^o Density Estimator.

Provable Maximum Entropy Manifold Exploration via Diffusion Models The Superposition of Diffusion Models Using the It\^o Density Estimator

Reference 57

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Observation 74172622-7986-486b-a2b7-2cc0b721b6a5 · outbound

This paper cites Deep unsupervised learning using nonequilibrium thermodynamics.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Deep unsupervised learning using nonequilibrium thermodynamics

Reference 58

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This paper cites and Ermon, S.

Provable Maximum Entropy Manifold Exploration via Diffusion Models and Ermon, S

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-15T19:48:18.527561Z

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Observation 33c7168c-3e19-4566-a68f-51d8be864db6 · outbound

This paper cites Score-Based Generative Modeling through Stochastic Differential Equations.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 60

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Observation 6815d5e4-2bc8-4792-8807-7be8a4fd4080 · outbound

This paper cites P., Batzolis, G., Deveney, T., and Sch \"o nlieb, C.-B.

Provable Maximum Entropy Manifold Exploration via Diffusion Models P., Batzolis, G., Deveney, T., and Sch \"o nlieb, C.-B

Reference 61

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Observation 91dad234-e292-46a8-bb2c-74292d64bbcb · outbound

This paper cites Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Fine-tuning of diffusion models via stochastic control: entropy regularization and beyond

Reference 62

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This paper cites Contractive Diffusion Probabilistic Models.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Contractive Diffusion Probabilistic Models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-15T19:48:18.540767Z

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Observation b3aed93b-3141-4cd1-beeb-c588fbe8bd8f · outbound

This paper cites Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control

Reference 64

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Observation 4a3f5504-efa9-4428-aa25-c55c7d85197e · outbound

This paper cites Feedback Efficient Online Fine-Tuning of Diffusion Models.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Feedback Efficient Online Fine-Tuning of Diffusion Models

Reference 65

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Observation 8fc0f21d-e3b9-4905-a42a-28e2167791f6 · outbound

This paper cites and Ye, J.

Provable Maximum Entropy Manifold Exploration via Diffusion Models and Ye, J

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:48:19.024049Z

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Observation b50b23ef-a741-4ddf-9381-10db01c7014c · outbound

This paper cites Don't Play Favorites: Minority Guidance for Diffusion Models.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Don't Play Favorites: Minority Guidance for Diffusion Models

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-15T19:48:18.555025Z

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Observation 2b3a03d9-18c9-4634-836e-6d381a4ae9c0 · outbound

This paper cites A connection between score matching and denoising autoencoders.

Provable Maximum Entropy Manifold Exploration via Diffusion Models A connection between score matching and denoising autoencoders

Reference 68

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Observation ed009bdf-bbbb-47cb-bc84-4aadd9da92e9 · outbound

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Provable Maximum Entropy Manifold Exploration via Diffusion Models Unresolved cited work

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-15T19:48:18.562079Z

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Observation 977d078a-8674-4964-9afa-af5afb1e10d4 · outbound

This paper cites Exploring low-toxicity chemical space with deep learning for molecular generation.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Exploring low-toxicity chemical space with deep learning for molecular generation

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:48:18.997786Z

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Observation 9a7a9e8f-927a-4bd4-8d58-bf9fce22db03 · outbound

This paper cites MatterGen: a generative model for inorganic materials design.

Provable Maximum Entropy Manifold Exploration via Diffusion Models MatterGen: a generative model for inorganic materials design

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-15T19:48:18.568494Z

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Observation 63ace8c6-a278-486b-bf3e-f0697eafbe16 · outbound

This paper cites Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-15T19:48:18.572913Z

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Observation 8b084fc9-18af-4060-a4d4-abdc4cae8da2 · outbound

This paper cites Toward understanding generative data augmentation.

Provable Maximum Entropy Manifold Exploration via Diffusion Models Toward understanding generative data augmentation

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:48:18.986306Z

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Pith citing papers

Observation 5e26d5c3-d3b4-4a8e-9152-0bd6e2e72714 · inbound

Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds cites this paper.

Intrinsic Wasserstein Rates for Score-Based Generative Models on Smooth Manifolds Provable Maximum Entropy Manifold Exploration via Diffusion Models

Reference 13

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