Paper Citation Record · LEDGER

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models

As of 21 August 2026, this Paper Citation Record lists 26 of 26 outbound references and 3 inbound Pith citation observations for arXiv:2505.05163.

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

pith.paper-citation-record.v1
2505.05163 v2

Coverage vector

measured 26 of 26 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T23:17:19.516560Z

measured 29 of 29 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T16:10:09.092184Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T22:46:35.968142Z

Reference resolution

26 of 26 outbound references displayed

verified exact2
verified fuzzy11
unresolved13
parse uncertain0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 170e064b-72fe-4eff-aac5-30657a53f91e · outbound

This paper cites Improved Probabilistic Image-Text Representations.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Improved Probabilistic Image-Text Representations

Reference 3

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Source-reported events for the cited work

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Observation e873bf03-b59c-4719-8edc-f4b0234ef632 · outbound

This paper cites Neural Processes.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Neural Processes

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:19.449715Z

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Observation 4f10b59a-caf6-453e-bf61-ae9b4460a077 · outbound

This paper cites Out-of-distribution detection for monocular depth esti- mation.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Out-of-distribution detection for monocular depth esti- mation

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:19.743497Z

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Observation 101bb0a9-152c-495b-9d44-83d181f0180a · outbound

This paper cites Automated flower classification over a large number of classes.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Automated flower classification over a large number of classes

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:19.735254Z

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Observation 130add35-5273-4f7d-b089-2560675006c6 · outbound

This paper cites CLIPVQA:Video Quality Assessment via CLIP.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models CLIPVQA:Video Quality Assessment via CLIP

Reference 16

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Source-reported events for the cited work

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Observation e79bfa54-9b3f-4a52-99c6-df75b2dbdf18 · outbound

This paper cites A.1 DATASETS For the experiments, we use MS-COCO, Flickr30k, CUB-200-2011, and Oxford Flowers 102 dataset.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models A.1 DATASETS For the experiments, we use MS-COCO, Flickr30k, CUB-200-2011, and Oxford Flowers 102 dataset

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:19.709722Z

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Observation cbc3c24b-d272-44f7-ab30-bf440b1bddc2 · outbound

This paper cites The dataset is split into 29,783 training images, with 1,000 images each in the validation and test sets.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models The dataset is split into 29,783 training images, with 1,000 images each in the validation and test sets

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:19.700725Z

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Observation d3f657b9-d102-422b-ba8f-7edd86a47788 · outbound

This paper cites an unresolved cited work.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:17:19.683197Z

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Observation 5a41dbb0-a3c8-4515-8b55-6ac605cea257 · outbound

This paper cites During training, we adapt these methods to process image and text embeddings derived from a frozen VLM.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models During training, we adapt these methods to process image and text embeddings derived from a frozen VLM

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:19.674212Z

Source-reported events for the cited work

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Observation 2fe7f8cc-6bc9-4627-ab97-fdc6fec57665 · outbound

This paper cites ForQ, we evaluated valuesQ∈{ 2, 5, 10, 20, 50, 128, 256}.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models ForQ, we evaluated valuesQ∈{ 2, 5, 10, 20, 50, 128, 256}

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:19.656634Z

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.

Observation 4b1859a7-a3b9-4f28-80a9-76c1afe38098 · outbound

This paper cites We also include a scenario where either the image or text is masked, introducing ambiguity.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models We also include a scenario where either the image or text is masked, introducing ambiguity

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:19.638616Z

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.

Observation f63819bd-04e1-44f6-b44e-263303118d06 · outbound

This paper cites an unresolved cited work.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:17:19.665585Z

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.

Observation 2b8a9243-dec3-4304-9ad5-420145278041 · outbound

This paper cites Finally, the learning rate of 1e−5 was selected based on a grid-search over values{1e−1, 1e−2, 1e−3, 1e−4, 1e−5, 1e−6}.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Finally, the learning rate of 1e−5 was selected based on a grid-search over values{1e−1, 1e−2, 1e−3, 1e−4, 1e−5, 1e−6}

Reference 400

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:19.647814Z

Source-reported events for the cited work

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Observation da342b04-67bc-4047-ba76-b700edda81c0 · outbound

This paper cites Online Zero-Shot Classification with CLIP.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Online Zero-Shot Classification with CLIP

Reference 1999

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:19.475360Z

Source-reported events for the cited work

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Observation a5bd9695-0085-4145-8669-08ca4a8d957a · outbound

This paper cites Learning Structured Semantic Embeddings for Visual Recognition.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Learning Structured Semantic Embeddings for Visual Recognition

Reference 2003

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:17:19.593215Z

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.

Observation eaa897a0-90de-408e-8a27-ba3a62ac9e10 · outbound

This paper cites Representation Learning with Contrastive Predictive Coding.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Representation Learning with Contrastive Predictive Coding

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:19.472568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7359546-403e-4f0c-bfc1-8d837ee559ea · outbound

This paper cites Probvlm: Probabilistic adapter for frozen vison-language models.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Probvlm: Probabilistic adapter for frozen vison-language models

Reference 2009

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:19.726516Z

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.

Observation 217321e1-6b03-42c8-a362-9ee96a6e6fd1 · outbound

This paper cites an unresolved cited work.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Unresolved cited work

Reference 2011

Resolution: unresolved; raw_fallback, observed 2026-08-15T23:17:19.692051Z

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.

Observation ba986d03-09e1-49e1-8c7e-0ad487a75524 · outbound

This paper cites SLURP: Side Learning Uncertainty for Regression Problems.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models SLURP: Side Learning Uncertainty for Regression Problems

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:19.491137Z

Source-reported events for the cited work

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Observation de35717b-996f-48c3-bc6f-be8969522248 · outbound

This paper cites Microsoft COCO Captions: Data Collection and Evaluation Server.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Microsoft COCO Captions: Data Collection and Evaluation Server

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:19.443376Z

Source-reported events for the cited work

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Observation ced93424-31d1-4ff2-9004-254223f508b6 · outbound

This paper cites LXMERT: Learning Cross-Modality Encoder Representations from Transformers.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:19.478813Z

Source-reported events for the cited work

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Observation 52115e83-c6b9-48d7-b368-6a9c3052a80e · outbound

This paper cites Hierarchy-based image embeddings for semantic image retrieval.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Hierarchy-based image embeddings for semantic image retrieval

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:19.751916Z

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.

Observation 845845c1-79a5-47e5-86e6-9b9eac81c653 · outbound

This paper cites Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models Generalised Gaussian Process Latent Variable Models (GPLVM) with Stochastic Variational Inference

Reference 2019

Resolution: verified exact; local_arxiv, observed 2026-08-15T23:17:19.605707Z

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.

Observation 787924a1-ecfb-43c6-b0f9-b5b92696c590 · outbound

This paper cites The caltech-ucsd birds-200- 2011 dataset.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models The caltech-ucsd birds-200- 2011 dataset

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T23:17:19.718024Z

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.

Observation 1e271b46-9c58-417a-80c2-adff425530ce · outbound

This paper cites GridCLIP: One-Stage Object Detection by Grid-Level CLIP Representation Learning.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models GridCLIP: One-Stage Object Detection by Grid-Level CLIP Representation Learning

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:19.466528Z

Source-reported events for the cited work

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Observation 05847589-ce2a-47fb-b0bb-fe9933a1dab1 · outbound

This paper cites VisualBERT: A Simple and Performant Baseline for Vision and Language.

Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models VisualBERT: A Simple and Performant Baseline for Vision and Language

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:19.463133Z

Source-reported events for the cited work

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Pith citing papers

Observation dec8983c-6a67-4c2f-a8b0-3749a9aec775 · inbound

Distance-informed Neural Processes cites this paper.

Distance-informed Neural Processes Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-05T16:10:09.092184Z

Source-reported events for the cited work

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Observation 2b4244f0-e312-488e-9839-72211bfd3173 · inbound

Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees cites this paper.

Remember with Confidence: Uncertainty Quantification for Spatio-temporal Memory with Probabilistic Guarantees Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-07-02T21:17:24.661700Z

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.

Observation e80c48f1-0dbd-488b-ba08-b909a5e77601 · inbound

General Incomplete Multimodal Learning via Dynamic Quality Perception cites this paper.

General Incomplete Multimodal Learning via Dynamic Quality Perception Probabilistic Embeddings for Frozen Vision-Language Models: Uncertainty Quantification with Gaussian Process Latent Variable Models

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-07-09T22:46:35.969699Z

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.