Paper Citation Record · LEDGER

Bayesian Comparisons Between Representations

As of 22 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2411.08739.

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

pith.paper-citation-record.v1
2411.08739 v3

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:30:46.734835Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+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

13 of 13 outbound references displayed

verified exact4
verified fuzzy5
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 477b3a9d-e8a0-4f5f-be5d-ff9f7cb1543e · outbound

This paper cites GULP: a prediction-based metric between representations.

Bayesian Comparisons Between Representations GULP: a prediction-based metric between representations

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:30:48.305061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dda4c154-e42a-49a5-8d22-7254a261b0bf · outbound

This paper cites One weird trick for parallelizing convolutional neural networks.

Bayesian Comparisons Between Representations One weird trick for parallelizing convolutional neural networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T21:30:46.435227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 94970efc-6387-4eb5-991b-20e43cdbd0bd · outbound

This paper cites (2017, April).

Bayesian Comparisons Between Representations (2017, April)

Reference 3

Resolution: verified exact; doi, observed 2026-08-12T21:30:47.094430Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 8614e0cf-05d6-4bdd-a3bd-003d02ea7bf4 · outbound

This paper cites an unresolved cited work.

Bayesian Comparisons Between Representations Unresolved cited work

Reference 6

Resolution: verified exact; doi, observed 2026-08-12T21:30:46.945349Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2c88ab44-7b30-45d3-a274-ac76fa09a2db · outbound

This paper cites an unresolved cited work.

Bayesian Comparisons Between Representations Unresolved cited work

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T21:30:46.234884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 274c654c-33a3-4e34-a9d1-787ec8e6f052 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Bayesian Comparisons Between Representations PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T21:30:46.485092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84232b23-4892-47da-bc94-76d6f61f5e61 · outbound

This paper cites Auto-Encoding Variational Bayes.

Bayesian Comparisons Between Representations Auto-Encoding Variational Bayes

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T21:30:46.334814Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c9c682c-a273-473f-9e58-bec25c5460b2 · outbound

This paper cites IMAGENET1K_V1.

Bayesian Comparisons Between Representations IMAGENET1K_V1

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:30:48.991337Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation c7a447a9-38b5-461a-8162-2ffc71c33d1f · outbound

This paper cites The displayed comparisons were chosen from the matrix of comparisons between all Alexnet lay- ers and all ResNet-18 layers, but both displayed comparisons remained within ResNet-18.

Bayesian Comparisons Between Representations The displayed comparisons were chosen from the matrix of comparisons between all Alexnet lay- ers and all ResNet-18 layers, but both displayed comparisons remained within ResNet-18

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:30:48.814968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation f4765268-c5f0-4aff-beac-0ba8e497cf96 · outbound

This paper cites For other applica- tions in machine learning these densities need to be approx- imated based on samples as well, which can severely bias these estimators (Murphy, 2022).

Bayesian Comparisons Between Representations For other applica- tions in machine learning these densities need to be approx- imated based on samples as well, which can severely bias these estimators (Murphy, 2022)

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:30:48.649540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 111e67e3-68a3-4170-ab38-00ea8a2374f3 · outbound

This paper cites IMAGENET1K_V1.

Bayesian Comparisons Between Representations IMAGENET1K_V1

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:30:49.165053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 2db75469-9e90-4720-91e3-afc5575dd210 · outbound

This paper cites For the signal to noise ratio, a discrete prior with 10 different equally likely values placed logarithmically between exp(−5) and 1 was used.

Bayesian Comparisons Between Representations For the signal to noise ratio, a discrete prior with 10 different equally likely values placed logarithmically between exp(−5) and 1 was used

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:30:48.474866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 5c7d26e1-d5fb-4774-a247-793fffa937d5 · outbound

This paper cites Duality of Bures and Shape Distances with Implications for Comparing Neural Representations.

Bayesian Comparisons Between Representations Duality of Bures and Shape Distances with Implications for Comparing Neural Representations

Reference 2664

Resolution: verified exact; local_arxiv, observed 2026-08-12T21:30:47.864874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Pith citing papers

No inbound Pith citation observations are available.