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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=sha256:984c8d420af49ff4b3dee58def29807fe29280c2079b3eff4a05a4a33260aa24

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=sha256:041727f080adc5ea07651273f87ae87c5d687796981d650ac279c4362891a66d

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:e02fe5878ae433b562d6e8a7b31ad122cf4b6c55d6b1e24e3426bd8fc4749559

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:cb44a976b50f1aa83ae360aa9f577cf8f34019ec63f924d66409a4da12569eef

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=sha256:6a0ba8f26325055beacb08f6c7338aaedc1bba176fec843020c560b0d61d558a

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=sha256:72589ebb0062b6a3b15dc53fec783614cd12c11f780ba79ed5fefe7c5c26615b

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=sha256:8cd884af4489929856c51ee8cb1b426f141fac5e2f27d5d5531447804fe3026e

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=sha256:24752abe62db0021b59d79bc757b530438769fa453453de9f428cf06c407f0ab

Pith citing papers

No inbound Pith citation observations are available.