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Paper Citation Record · LEDGER

Decafs: Disentangled Conditional adversarial Flows

As of 8 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2607.18755.

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

pith.paper-citation-record.v1
2607.18755 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T14:34:32.168996Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+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

22 of 22 outbound references displayed

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  • verified fuzzy0
  • unresolved20
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External citation measurements

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Outbound references

Observation f1bac322-c986-4575-a722-dcd490362594 · outbound

This paper cites Deep Variational Information Bottleneck.

Decafs: Disentangled Conditional adversarial Flows Deep Variational Information Bottleneck

Reference 1

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Observation 768090ea-e789-4c8d-88cb-046c425ba442 · outbound

This paper cites RDKit: Open-source cheminformatics.

Decafs: Disentangled Conditional adversarial Flows RDKit: Open-source cheminformatics

Reference 7

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Observation 7c80d4c3-c547-42af-be64-e6ed3e0761da · outbound

This paper cites A property-guided diffusion model for generating molecular graphs.

Decafs: Disentangled Conditional adversarial Flows A property-guided diffusion model for generating molecular graphs

Reference 10

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Observation 2f88da52-ee56-46a8-b447-06a680ffc272 · outbound

This paper cites The Transitive Information Theory and its Application to Deep Generative Models.

Decafs: Disentangled Conditional adversarial Flows The Transitive Information Theory and its Application to Deep Generative Models

Reference 13

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Observation 2aafba98-a2dd-4335-a695-05c783671f3d · outbound

This paper cites Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations.

Decafs: Disentangled Conditional adversarial Flows Improving Denoising Diffusion Probabilistic Models via Exploiting Shared Representations

Reference 14

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Observation b5448cb1-1c9e-465b-a616-5cc076b7f492 · outbound

This paper cites GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation.

Decafs: Disentangled Conditional adversarial Flows GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation

Reference 15

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Observation 0a1f985e-0c68-47bc-8216-107bbfc724e7 · outbound

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

Decafs: Disentangled Conditional adversarial Flows Score-Based Generative Modeling through Stochastic Differential Equations

Reference 16

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Observation 0d632c6b-12ec-4602-bf10-99de27ae06d5 · outbound

This paper cites AbODE: Ab Initio Antibody Design using Conjoined ODEs.

Decafs: Disentangled Conditional adversarial Flows AbODE: Ab Initio Antibody Design using Conjoined ODEs

Reference 17

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Observation de4ced7e-9b5f-4bd7-abb4-3ec918755445 · outbound

This paper cites Uncovering the disentanglement capability in text-to-image diffusion models.

Decafs: Disentangled Conditional adversarial Flows Uncovering the disentanglement capability in text-to-image diffusion models

Reference 19

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Observation c0d7c059-0c65-4c1f-89c0-bd031b79c68e · outbound

This paper cites Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation.

Decafs: Disentangled Conditional adversarial Flows Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation

Reference 21

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Observation 4c484c7f-9298-4b78-99a9-455654feddf0 · outbound

This paper cites an unresolved cited work.

Decafs: Disentangled Conditional adversarial Flows Unresolved cited work

Reference 32

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Observation 65d923bd-9ef2-4e8d-be61-159b04793475 · outbound

This paper cites Variational Inference of Disentangled Latent Concepts from Unlabeled Observations.

Decafs: Disentangled Conditional adversarial Flows Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

Reference 2013

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Observation ae4e5a42-3b84-47a0-8498-3c3f30f042c0 · outbound

This paper cites Density estimation using Real NVP.

Decafs: Disentangled Conditional adversarial Flows Density estimation using Real NVP

Reference 2014

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source=pdf_text observed=2026-08-01T14:34:30.769611Z digest=sha256:d9a7b2af99723c5532e60ab12cb65e6d133daccb159e194f9e60d177557d03ee

Observation 0bc689ae-b850-40e8-9d9a-b8d133f5357b · outbound

This paper cites Yann LeCun.

Decafs: Disentangled Conditional adversarial Flows Yann LeCun

Reference 2015

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Source-reported events for the cited work

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

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Observation 06c7a1c2-9ff0-408d-978b-50f45e89065c · outbound

This paper cites Classifier-Free Diffusion Guidance.

Decafs: Disentangled Conditional adversarial Flows Classifier-Free Diffusion Guidance

Reference 2017

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Observation 74c17ce5-4ae7-4b80-b0a1-721aa4c2e96d · outbound

This paper cites Auto-Encoding Variational Bayes.

Decafs: Disentangled Conditional adversarial Flows Auto-Encoding Variational Bayes

Reference 2018

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Observation 237a5efd-b129-4827-b501-253c12480082 · outbound

This paper cites DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models.

Decafs: Disentangled Conditional adversarial Flows DisDiff: Unsupervised Disentanglement of Diffusion Probabilistic Models

Reference 2019

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Observation d743b7f6-78f3-46a7-8f76-7bdc3b89d2c5 · outbound

This paper cites Categorical Normalizing Flows via Continuous Transformations.

Decafs: Disentangled Conditional adversarial Flows Categorical Normalizing Flows via Continuous Transformations

Reference 2020

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Observation aadba612-92c0-42bb-b069-d6315702e25c · outbound

This paper cites NICE: Non-linear Independent Components Estimation.

Decafs: Disentangled Conditional adversarial Flows NICE: Non-linear Independent Components Estimation

Reference 2021

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Observation 4636d57d-3e67-429b-a850-d9a96444abda · outbound

This paper cites Conditional Generative Adversarial Nets.

Decafs: Disentangled Conditional adversarial Flows Conditional Generative Adversarial Nets

Reference 2022

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Observation db13b808-5caa-44d5-bc9a-7061b29ccf2a · outbound

This paper cites Improving Robustness and Generality of NLP Models Using Disentangled Representations.

Decafs: Disentangled Conditional adversarial Flows Improving Robustness and Generality of NLP Models Using Disentangled Representations

Reference 2023

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Observation 17f90496-c6b1-4c49-b448-e58486aeea8d · outbound

This paper cites Emile Mathieu, Tom Rainforth, N Siddharth, and Yee Whye Teh.

Decafs: Disentangled Conditional adversarial Flows Emile Mathieu, Tom Rainforth, N Siddharth, and Yee Whye Teh

Reference 2024

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Pith citing papers

No inbound Pith citation observations are available.