Paper Citation Record · LEDGER

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data

As of 10 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2507.04216.

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

pith.paper-citation-record.v1
2507.04216 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:58:42.539518Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

13 of 13 outbound references displayed

verified exact1
verified fuzzy2
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 285be720-3ee8-4275-b0ba-641f6e9f9f36 · outbound

This paper cites The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data The Kernel Mixture Network: A Nonparametric Method for Conditional Density Estimation of Continuous Random Variables

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:41.166200Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ed2e27a-4843-44da-9f7a-5a92e24b0d36 · outbound

This paper cites SGDR: Stochastic Gradient Descent with Warm Restarts.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data SGDR: Stochastic Gradient Descent with Warm Restarts

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:41.932909Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d45714ff-5cb3-4b6d-93c0-77de76bd69bb · outbound

This paper cites UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Reference 1979

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:42.010984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 776da2ea-3dde-4cd2-b73c-b8d37ffe0054 · outbound

This paper cites Generalized outlier detection with flexible kernel density estimates.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data Generalized outlier detection with flexible kernel density estimates

Reference 1997

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:43.039898Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9b5ca177-5ddb-4ff9-837c-8cfff8ab1fcf · outbound

This paper cites Aop: An anti-overfitting pretreatment for practical image-based plant diagnosis.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data Aop: An anti-overfitting pretreatment for practical image-based plant diagnosis

Reference 2000

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:58:43.264773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 38d833e8-cf5a-4858-b44b-5862d7dd831d · outbound

This paper cites Density estimation using Real NVP.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data Density estimation using Real NVP

Reference 2005

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:41.249049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cc5cdabd-e214-4d78-ab68-b2242d1e58dd · outbound

This paper cites FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

Reference 2007

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:41.396582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 763e5c7e-dc4b-4e3f-a253-bc735088e5eb · outbound

This paper cites Deep Mixtures of Factor Analysers.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data Deep Mixtures of Factor Analysers

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:42.407289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation efe6922a-434a-46ed-9f53-65503ebdced5 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:42.539518Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6e8b6b5-7786-4e7d-b667-64a0e63a5446 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data Adam: A Method for Stochastic Optimization

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:41.841479Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b8e562e7-844e-4469-8ca7-36713aab8c82 · outbound

This paper cites Evaluating Aleatoric Uncertainty via Conditional Generative Models.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data Evaluating Aleatoric Uncertainty via Conditional Generative Models

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:41.690740Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e34883e6-0f6a-4c01-9b3f-4ccd9e82b3a1 · outbound

This paper cites A Critique of Self-Expressive Deep Subspace Clustering.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data A Critique of Self-Expressive Deep Subspace Clustering

Reference 2017

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:58:42.810706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7c48e380-f5f9-4428-9c6c-2cb50279b1d7 · outbound

This paper cites Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks.

Normalizing Flow to Augmented Posterior: Conditional Density Estimation with Interpretable Dimension Reduction for High Dimensional Data Conditional Density Estimation with Neural Networks: Best Practices and Benchmarks

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-06T19:58:42.117387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.