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Paper Citation Record · LEDGER

How big can style be? Addressing high dimensionality for recommending with style

As of 14 August 2026, this Paper Citation Record lists 8 of 8 outbound references and 0 inbound Pith citation observations for arXiv:1908.10642.

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

pith.paper-citation-record.v1
1908.10642 v1

Coverage vector

measured 8 of 8 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-14T10:41:32.973069Z

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

8 of 8 outbound references displayed

  • verified exact2
  • verified fuzzy0
  • unresolved5
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1d428d8d-9e7c-4574-b09d-3c615aec8df3 · outbound

This paper cites A Neural Algorithm of Artistic Style.

How big can style be? Addressing high dimensionality for recommending with style A Neural Algorithm of Artistic Style

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:32.937816Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T10:41:32.937816Z digest=sha256:9e84b16b55b1b421278cb52a83aa8307729915f18f0797b5b144d140b9ddde01

Observation 6e3c7329-e70d-4479-b8f8-f0a2a651892b · outbound

This paper cites Texture Synthesis Using Convolutional Neural Networks.

How big can style be? Addressing high dimensionality for recommending with style Texture Synthesis Using Convolutional Neural Networks

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:32.943056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T10:41:32.943056Z digest=sha256:6d3553ae3d3a6886caaf0d5ec73dd8a480e04284101e960470368586af7d2f37

Observation 7b572315-e154-47d8-b92c-247e7c71c6e7 · outbound

This paper cites an unresolved cited work.

How big can style be? Addressing high dimensionality for recommending with style Unresolved cited work

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:32.947979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T10:41:32.947979Z digest=sha256:bfbd90347f94ce209a4432fbb4625279ac1e6ad644111f9faa8d0d5b946b21f4

Observation 8a579551-f34b-46bd-982f-e913d96e465b · outbound

This paper cites Learning Fashion Compatibility with Bidirectional LSTMs.

How big can style be? Addressing high dimensionality for recommending with style Learning Fashion Compatibility with Bidirectional LSTMs

Reference 4

Resolution
verified exact
local_arxiv, observed 2026-08-14T10:41:33.085201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-14T10:41:32.952584Z digest=sha256:1e7eef0448ae3e8d77d58a74d6475aaf21703d60387b04c37696c612e04c46b2

Observation a679ebe3-d9da-41cb-b829-dd3a7c837318 · outbound

This paper cites Style2Vec: Representation Learning for Fashion Items from Style Sets.

How big can style be? Addressing high dimensionality for recommending with style Style2Vec: Representation Learning for Fashion Items from Style Sets

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:32.957870Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T10:41:32.957870Z digest=sha256:2a7df5765daf6f837e599a35c2f898d49566749597d4ed7cb3a3f423da32bfc6

Observation 16b72b06-8072-42fd-b4d8-6965c3252dc9 · outbound

This paper cites Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data.

How big can style be? Addressing high dimensionality for recommending with style Mining Fashion Outfit Composition Using An End-to-End Deep Learning Approach on Set Data

Reference 6

Resolution
verified exact
local_arxiv, observed 2026-08-14T10:41:33.049905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-14T10:41:32.963209Z digest=sha256:58a48cc0d3b1a5b67077b59fe305b0c85e726bf2545781ca3e227ec565819484

Observation 0e8e22ad-3bce-4bca-8e91-ee8df058f497 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

How big can style be? Addressing high dimensionality for recommending with style Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-14T10:41:32.968606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-14T10:41:32.968606Z digest=sha256:ea260f1565d5c735183219c2944b5a333fbb414e0cfc8e6e4e1703767483661e

Observation cbc279f1-a661-45c3-ab91-458c5e4cbf30 · outbound

This paper cites Texture Synthesis Using Shallow Convolutional Networks with Random Filters.

How big can style be? Addressing high dimensionality for recommending with style Texture Synthesis Using Shallow Convolutional Networks with Random Filters

Reference 8

Resolution
metadata mismatch
local_arxiv, observed 2026-08-14T10:41:33.013442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-08-14T10:41:32.973069Z digest=sha256:396bd7d5127e1950266dfbc4041e26edb2a7b6cfdedadc099fe0ba48e5f59b79

Pith citing papers

No inbound Pith citation observations are available.