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Paper Citation Record · LEDGER

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

As of 20 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 2026-08-20T06:33:59.587034+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=sha256:152eba4c183efd88d499b41c6460b6a038ced768d12e9f55e39064d8dd7683d1

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:fbd7498c5e67b4f56e6947466608cf8d45fcec424636a68a5a776fd4270fabd4

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=sha256:3def9ac4d2fb685fa241660b9104185c6601bddd1c24a190244c0dfaca4e26b5

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T10:41:32.952584Z digest=sha256:76fa795e58f83e32254781766b1ba0028cf63b0aaf738ec4a103479aa87cf84e

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=sha256:48b5ee761bc9a65ea342dfa6d5b68244fdf4eba56f4874b37923e05b18604422

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T10:41:32.963209Z digest=sha256:531877a152d4193baa44ad22bf1cfacbf52368c21ef54b908558a6440c2a5430

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=sha256:0f8b3a26dff74c57e0284a610778b6944b25268d834265ffdee279a2c2251025

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 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-14T10:41:32.973069Z digest=sha256:aef586a7be833afb41fb2edfa4483e3c5d2cc650e3a49c4b21e6bed811585def

Pith citing papers

No inbound Pith citation observations are available.