Paper Citation Record · LEDGER

Incorporating Inductive Biases to Energy-based Generative Models

As of 16 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2505.01111.

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

pith.paper-citation-record.v1
2505.01111 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:31:59.930909Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

verified exact2
verified fuzzy6
unresolved16
parse uncertain0
malformed identifier0
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External citation measurements

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Outbound references

Observation e61e7647-ee39-40b5-8542-b221a9a331e1 · outbound

This paper cites Residual Energy-Based Models for Text Generation.

Incorporating Inductive Biases to Energy-based Generative Models Residual Energy-Based Models for Text Generation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.850583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0bc1257-42aa-4d51-9040-13c4a7a8e1cd · outbound

This paper cites Classification of garments from fashion mnist dataset using cnn lenet-5 architecture.

Incorporating Inductive Biases to Energy-based Generative Models Classification of garments from fashion mnist dataset using cnn lenet-5 architecture

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:32:00.329884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 22a5c631-7678-4ea6-9f03-15447024d306 · outbound

This paper cites DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic Models.

Incorporating Inductive Biases to Energy-based Generative Models DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.871855Z

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Observation 9db1c3b5-ada2-4c5a-aec6-981df571331a · outbound

This paper cites GraphEBM: Molecular Graph Generation with Energy-Based Models.

Incorporating Inductive Biases to Energy-based Generative Models GraphEBM: Molecular Graph Generation with Energy-Based Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.890055Z

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Observation db5a431d-1373-4f0b-a4c6-1ae1b375ff0a · outbound

This paper cites Efficient learning of generativemodelsviafinite-differencescorematching.

Incorporating Inductive Biases to Energy-based Generative Models Efficient learning of generativemodelsviafinite-differencescorematching

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:32:00.318618Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 7087b8b8-0cee-4045-a141-c276598f4fa5 · outbound

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

Incorporating Inductive Biases to Energy-based Generative Models Score-Based Generative Modeling through Stochastic Differential Equations

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.903537Z

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Observation b1d496ce-17de-41b3-b191-9659d67efcb3 · outbound

This paper cites Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, and Ying Nian Wu.

Incorporating Inductive Biases to Energy-based Generative Models Jianwen Xie, Zilong Zheng, Ruiqi Gao, Wenguan Wang, Song-Chun Zhu, and Ying Nian Wu

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.907737Z

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Observation 927a6c46-d461-4cb1-8d6c-06e45d88b3f6 · outbound

This paper cites Generative pointnet: Deep energy- based learning on unordered point sets for 3d generation, reconstruction and classification.

Incorporating Inductive Biases to Energy-based Generative Models Generative pointnet: Deep energy- based learning on unordered point sets for 3d generation, reconstruction and classification

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:32:00.307089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ca5d149a-01bb-4ac0-85c0-b130f2faff0f · outbound

This paper cites GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation.

Incorporating Inductive Biases to Energy-based Generative Models GeoDiff: a Geometric Diffusion Model for Molecular Conformation Generation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.915670Z

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Observation 144b3ae1-524d-4f63-a2ba-7ddc671cd326 · outbound

This paper cites FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model.

Incorporating Inductive Biases to Energy-based Generative Models FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.919700Z

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Observation 8229167c-5c49-45be-9c77-9c051f37da9a · outbound

This paper cites Latent Diffusion Energy-Based Model for Interpretable Text Modeling.

Incorporating Inductive Biases to Energy-based Generative Models Latent Diffusion Energy-Based Model for Interpretable Text Modeling

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.923604Z

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Unavailable: canonical work link unavailable.

Observation 1d808923-0b0f-43c0-9921-f210eb498cea · outbound

This paper cites If η is at a local maximum of the data log-likelihood, thenEpθ [T(x)] = 1 N ∑N 1 T(xi).

Incorporating Inductive Biases to Energy-based Generative Models If η is at a local maximum of the data log-likelihood, thenEpθ [T(x)] = 1 N ∑N 1 T(xi)

Reference 1997

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:32:00.293866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e32355dc-38bb-4fd7-8e76-0d64755e07d8 · outbound

This paper cites ShapeNet: An Information-Rich 3D Model Repository.

Incorporating Inductive Biases to Energy-based Generative Models ShapeNet: An Information-Rich 3D Model Repository

Reference 2004

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.836290Z

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Observation 52bc9916-4353-4936-aba1-cc13dc919874 · outbound

This paper cites Performance comparison of three parameter estimation methods on heavily censored data.

Incorporating Inductive Biases to Energy-based Generative Models Performance comparison of three parameter estimation methods on heavily censored data

Reference 2005

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:32:00.342140Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 121fdc10-94b4-493d-8d0e-05b145444921 · outbound

This paper cites Enhancing Diffusion-based Point Cloud Generation with Smoothness Constraint.

Incorporating Inductive Biases to Energy-based Generative Models Enhancing Diffusion-based Point Cloud Generation with Smoothness Constraint

Reference 2006

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.886000Z

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Observation c0c73a5c-b252-4680-a8e6-e844e871ed0c · outbound

This paper cites Energy-Based Models for Code Generation under Compilability Constraints.

Incorporating Inductive Biases to Energy-based Generative Models Energy-Based Models for Code Generation under Compilability Constraints

Reference 2010

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:32:00.157757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation ab25377d-30cd-402f-b585-bd9bbdada360 · outbound

This paper cites NVDiff: Graph Generation through the Diffusion of Node Vectors.

Incorporating Inductive Biases to Energy-based Generative Models NVDiff: Graph Generation through the Diffusion of Node Vectors

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.840590Z

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Observation f99de31b-71d2-483e-931b-5f4a9d22a18b · outbound

This paper cites EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations.

Incorporating Inductive Biases to Energy-based Generative Models EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.927357Z

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Unavailable: canonical work link unavailable.

Observation 7ab00468-f793-4cef-bcf3-a98f31f822b6 · outbound

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

Incorporating Inductive Biases to Energy-based Generative Models How to Train Your Energy-Based Models

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.898917Z

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Observation f09c6c69-ca6b-46f9-ad40-de35feabdc74 · outbound

This paper cites A Distributional Approach to Controlled Text Generation.

Incorporating Inductive Biases to Energy-based Generative Models A Distributional Approach to Controlled Text Generation

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.867591Z

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Observation bdee8fce-2f1d-4518-a7ba-d1696a86c287 · outbound

This paper cites Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection.

Incorporating Inductive Biases to Energy-based Generative Models Hybrid Energy Based Model in the Feature Space for Out-of-Distribution Detection

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-16T04:32:00.140338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 446d23eb-a8d4-403d-9d58-fdc8fcc7d1c9 · outbound

This paper cites Improving Diffusion Models for Inverse Problems using Manifold Constraints.

Incorporating Inductive Biases to Energy-based Generative Models Improving Diffusion Models for Inverse Problems using Manifold Constraints

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.844495Z

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Observation 4451a53c-9364-4074-ae89-0a93d959ef13 · outbound

This paper cites Structured prediction energy networks.

Incorporating Inductive Biases to Energy-based Generative Models Structured prediction energy networks

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T04:32:00.354429Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1edaa987-c323-41eb-bd20-f7f0e099b74a · outbound

This paper cites Aapo Hyvärinen and Peter Dayan.

Incorporating Inductive Biases to Energy-based Generative Models Aapo Hyvärinen and Peter Dayan

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T04:31:59.854729Z

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Pith citing papers

No inbound Pith citation observations are available.