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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=sha256:8ef55a13e713065eb168f38c214a575324aeb91207349eaf72672e9db59e9d19

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:fb29c74b32b904e232929f1326099935d52dc805c41c183a4e09cba0cc6cbb30

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=sha256:6e14d3b9d4eec6dc821345daefc75875760a4ec46ac82bcbba68003fc54e6eed

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=sha256:6f991644b5cadb83a07aa24d4c2ac409b3da4d3e35f4134ca06d85e39a13b301

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=sha256:57b65e7be788d75db2394107cdb1b7075d4d64771db3a0bbd36a87ab899a0353

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:ecca1d99b0f1c058cb360753e3638ea9e29648f98a410d905fdb26649225f323

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=sha256:1c47ed4c6fb423c7b7cc00f7bec92a51edcaab0f2bb2fd1e60477d83fa074920

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=sha256:0d7f689213c7975b6608c509a97f3c2232a9f10fe68bf4591fdba63e71323a58

Pith citing papers

No inbound Pith citation observations are available.