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Paper Citation Record · LEDGER

High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2106.03609.

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

pith.paper-citation-record.v1
2106.03609 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:44:34.244774Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T13:01:23.706994Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 04386cb1-a431-411e-8d1c-201f480448c9 · inbound

Dimensionality Reduction Techniques for Global Bayesian Optimisation cites this paper.

Dimensionality Reduction Techniques for Global Bayesian Optimisation High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-11T17:18:07.007906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:18:07.007906Z digest=sha256:c6988db3a01aba103081ea153337fbccc789aecbd1f0405322587194dc4fc525

Observation a5311b50-8f1d-47c2-a6ce-8b7ccb04e4a4 · inbound

Learned Offline Query Planning via Bayesian Optimization cites this paper.

Learned Offline Query Planning via Bayesian Optimization High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-08T20:09:59.959747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T20:09:59.959747Z digest=sha256:c5c734f61f6ddadbee6ece0bdcd96cb78c0fb321b8c86000330b71dc3ead9b00

Observation 46d03996-8e96-4da0-87ea-f863ef8609ed · inbound

Latent Bayesian Optimization via Autoregressive Normalizing Flows cites this paper.

Latent Bayesian Optimization via Autoregressive Normalizing Flows High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-16T11:44:34.244774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:44:34.244774Z digest=sha256:d04e4ad40c34b0c459904d7bf43c346ac8441ab87e2f7f7666f41afa3ad40f80

Observation 6b8e8280-6e82-4143-8bcd-c8fd07ac6817 · inbound

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces cites this paper.

Return of the Latent Space COWBOYS: Re-thinking the use of VAEs for Bayesian Optimisation of Structured Spaces High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-06T20:09:09.551163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T20:09:09.551163Z digest=sha256:0b3a31d43227fc34e28ed19781f35ce2d4ff53cad0a2159a6eaf2c13b8de69f1

Observation 5d8c6bff-c68e-4c3b-b18e-b3fa420a0b13 · inbound

Natural Evolutionary Search meets Probabilistic Numerics cites this paper.

Natural Evolutionary Search meets Probabilistic Numerics High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T18:51:16.433946Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:51:16.433946Z digest=sha256:5d781d748aacdb6d6fbb26edacceb82fc6d13efefb77f2ac5c6eb3ffe17c767d

Observation c726dc36-5b44-49ef-87c1-86a32af90316 · inbound

Sample-Efficient Optimisation over the Outputs of Generative Models cites this paper.

Sample-Efficient Optimisation over the Outputs of Generative Models High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-18T13:01:23.709758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-18T12:58:33.173828Z digest=sha256:5225ca6b3168e4df11e279f11115bf57ab0bc6d030260d618fe2d7aeddfc16b0

Observation b5fa8312-e65f-4543-99d8-94e19c0f9305 · inbound

Regret Analysis of Guided Diffusion for Black-Box Optimization over Structured Inputs cites this paper.

Regret Analysis of Guided Diffusion for Black-Box Optimization over Structured Inputs High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-12T07:11:23.324568Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-05-12T03:40:48.470892Z digest=sha256:cb49aa9e5289b598434c9ff4b457bdf3a375859b8a2b3dbc950924c9850ac3f8

Observation 0013a60f-1e8c-46d2-a8ca-93b3d2e99c88 · inbound

Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression cites this paper.

Optimization of time-consuming experimental conditions using pseudo-experimental data guided by adaptive polynomial regression High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-01T05:27:39.454377Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T05:27:39.454377Z digest=sha256:dcb70475f4defa5e567ef9b2c711ad1528e566ba4ec7d1d69a6ad2f1f752e906