Paper Citation Record · LEDGER

Likelihood Contribution based Multi-scale Architecture for Generative Flows

As of 15 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:1908.01686.

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

pith.paper-citation-record.v1
1908.01686 v3

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T15:11:14.334476Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T11:23:56.932223Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-14T11:23:57.302151Z

Reference resolution

20 of 20 outbound references displayed

verified exact1
verified fuzzy3
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 553c7ec8-76f5-47a2-bf90-417f712e71fe · outbound

This paper cites Semi-Conditional Normalizing Flows for Semi-Supervised Learning.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Semi-Conditional Normalizing Flows for Semi-Supervised Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.194788Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab26f721-b03b-4a78-8bb7-3593ad33e291 · outbound

This paper cites Datasets: We perform experiments on four benchmarked image datasets: CIFAR-10 (Krizhevsky, 2009), Imagenet (Russakovsky et al.,.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Datasets: We perform experiments on four benchmarked image datasets: CIFAR-10 (Krizhevsky, 2009), Imagenet (Russakovsky et al.,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:11:14.760813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation f840f1be-34e5-4dc1-9534-e251b0754a18 · outbound

This paper cites Generating Sequences With Recurrent Neural Networks.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Generating Sequences With Recurrent Neural Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.234864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d67bee5f-350a-46b3-90a2-06b672d61bb2 · outbound

This paper cites Emerging Convolutions for Generative Normalizing Flows.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Emerging Convolutions for Generative Normalizing Flows

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-14T15:11:14.560757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 6cb7dd7d-a49e-4aea-8c02-11666e23415c · outbound

This paper cites A Style-Based Generator Architecture for Generative Adversarial Networks.

Likelihood Contribution based Multi-scale Architecture for Generative Flows A Style-Based Generator Architecture for Generative Adversarial Networks

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.253726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ea0df73-d73c-4e54-a52a-75907e41b383 · outbound

This paper cites Auto-Encoding Variational Bayes.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Auto-Encoding Variational Bayes

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.268408Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ca7c496-9e0d-4694-be19-17ab2332cad9 · outbound

This paper cites Semi-Supervised Learning with Generative Adversarial Networks.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Semi-Supervised Learning with Generative Adversarial Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.280522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68dc640a-00a0-4c7f-bbec-11a4f63c0392 · outbound

This paper cites Pixel Recurrent Neural Networks.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Pixel Recurrent Neural Networks

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.287115Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 480137c1-feec-459b-812d-ded07b798fa6 · outbound

This paper cites ImageNet Large Scale Visual Recognition Challenge.

Likelihood Contribution based Multi-scale Architecture for Generative Flows ImageNet Large Scale Visual Recognition Challenge

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.301928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc731299-a34a-4b5c-afa5-655b90d0ab24 · outbound

This paper cites PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications.

Likelihood Contribution based Multi-scale Architecture for Generative Flows PixelCNN++: Improving the PixelCNN with Discretized Logistic Mixture Likelihood and Other Modifications

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.308857Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16d2a607-d35b-4533-8438-b6c758e47528 · outbound

This paper cites an unresolved cited work.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Unresolved cited work

Reference 19

Resolution: unresolved; raw_fallback, observed 2026-08-14T15:11:14.779718Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 4fba8725-c56f-4dde-bf3e-a55ea8b83995 · outbound

This paper cites Pre-processing: For CelebA, we take a central crop of 148× 148 then resize it to 64×.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Pre-processing: For CelebA, we take a central crop of 148× 148 then resize it to 64×

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:11:14.742173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation ec6aa7d5-be2d-4764-bba8-2e380382c932 · outbound

This paper cites The sample allocation for training and validation were done as per the ofﬁcial allocation for the datasets.

Likelihood Contribution based Multi-scale Architecture for Generative Flows The sample allocation for training and validation were done as per the ofﬁcial allocation for the datasets

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T15:11:14.724645Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

Observation 9aaf2dd9-5667-43c8-8fdc-47b92af25c0a · outbound

This paper cites PixelSNAIL: An Improved Autoregressive Generative Model.

Likelihood Contribution based Multi-scale Architecture for Generative Flows PixelSNAIL: An Improved Autoregressive Generative Model

Reference 1995

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.202251Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1bc2c39e-e44f-429b-8073-af3f68908e6b · outbound

This paper cites VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation.

Likelihood Contribution based Multi-scale Architecture for Generative Flows VideoFlow: A Conditional Flow-Based Model for Stochastic Video Generation

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.274648Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff64f107-2880-4a35-a187-03d8b0522954 · outbound

This paper cites Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.241001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2a95501-3192-4bdc-a96b-d794a4ff05da · outbound

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

Likelihood Contribution based Multi-scale Architecture for Generative Flows NICE: Non-linear Independent Components Estimation

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.209603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5e30d06-8175-4161-bf4c-12150b8ea048 · outbound

This paper cites Density estimation using Real NVP.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Density estimation using Real NVP

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.222751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99a5a27b-aa6f-484f-b22c-9a9b1d5a1873 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Likelihood Contribution based Multi-scale Architecture for Generative Flows Adam: A Method for Stochastic Optimization

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.262682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation df7eca7d-06a2-4e85-bac6-49d915584ebd · outbound

This paper cites GANSynth: Adversarial Neural Audio Synthesis.

Likelihood Contribution based Multi-scale Architecture for Generative Flows GANSynth: Adversarial Neural Audio Synthesis

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-14T15:11:14.228608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation fa55dfdd-7c39-483c-b797-caaafe12be40 · inbound

Normalizing Flows: An Introduction and Review of Current Methods cites this paper.

Normalizing Flows: An Introduction and Review of Current Methods Likelihood Contribution based Multi-scale Architecture for Generative Flows

Reference 2018

Resolution: metadata mismatch; local_arxiv, observed 2026-08-14T11:23:57.306433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.