Paper Citation Record · LEDGER

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS)

As of 20 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:2508.20322.

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

pith.paper-citation-record.v1
2508.20322 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:52:17.237081Z

measured 45 of 45 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

45 of 45 outbound references displayed

verified exact2
verified fuzzy25
unresolved18
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f204994e-c47b-4cfb-8983-cd8166180dde · outbound

This paper cites Aharon, M.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Aharon, M

Reference 1

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Source-reported events for the cited work

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Observation 945d6c30-49ec-4c0e-882b-81adfa97262c · outbound

This paper cites Effective conditioned and composed image retrieval combining CLIP -based features.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Effective conditioned and composed image retrieval combining CLIP -based features

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:17.049618Z

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Observation 5eb6bc02-82e6-4e8f-aef0-7126eeb30266 · outbound

This paper cites Calmon, and Himabindu Lakkaraju.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Calmon, and Himabindu Lakkaraju

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:18.155849Z

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Observation d5b120c1-4489-4ab4-9cae-7f2adf738bb5 · outbound

This paper cites A fast non-negativity-constrained least squares algorithm.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) A fast non-negativity-constrained least squares algorithm

Reference 4

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Observation deef83be-438b-4a56-bbd9-7c90abf65a8d · outbound

This paper cites Multidimensional independent component analysis.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Multidimensional independent component analysis

Reference 5

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Observation ec778c35-e4bc-48f1-a08b-c0500b0b201b · outbound

This paper cites Unsupervised learning of visual features by contrasting cluster assignments.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Unsupervised learning of visual features by contrasting cluster assignments

Reference 6

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Source-reported events for the cited work

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Observation f74bd542-9d82-4e3b-acc1-9864c7ec6717 · outbound

This paper cites Image retrieval for complex queries using knowledge embedding.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Image retrieval for complex queries using knowledge embedding

Reference 7

Resolution: verified exact; doi, observed 2026-08-15T16:52:17.297413Z

Source-reported events for the cited work

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Observation 2fe0c736-18ac-48c9-a6b5-9a7989222ae3 · outbound

This paper cites A simple framework for contrastive learning of visual representations.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) A simple framework for contrastive learning of visual representations

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:18.116153Z

Source-reported events for the cited work

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Observation b6aa6175-d23f-4331-955d-2db6a5fd0721 · outbound

This paper cites Improved Baselines with Momentum Contrastive Learning.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Improved Baselines with Momentum Contrastive Learning

Reference 9

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Source-reported events for the cited work

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Observation 03795481-96b3-4a71-8f8a-24699f5a8a4e · outbound

This paper cites An empirical study of training self-supervised vision transformers.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) An empirical study of training self-supervised vision transformers

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:18.101906Z

Source-reported events for the cited work

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Observation 1fff2dc7-e995-4423-b14c-b17a262f0d9d · outbound

This paper cites Embedding arithmetic of multimodal queries for image retrieval.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Embedding arithmetic of multimodal queries for image retrieval

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:17.092446Z

Source-reported events for the cited work

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Observation d6366d6c-1b66-48b5-b078-e9112e658533 · outbound

This paper cites Convex and semi-nonnegative matrix factorizations.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Convex and semi-nonnegative matrix factorizations

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:18.087112Z

Source-reported events for the cited work

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Observation f82db7fc-0bc1-4932-87ea-08226c9f29de · outbound

This paper cites The CLIP model is secretly an image-to-prompt converter.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) The CLIP model is secretly an image-to-prompt converter

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:18.072921Z

Source-reported events for the cited work

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Observation 69d04582-8c4d-48e7-a1aa-e1770edbaf66 · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Taming transformers for high-resolution image synthesis

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:17.105716Z

Source-reported events for the cited work

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Observation ca68b214-ad3d-4da8-8059-3701bb5a9d60 · outbound

This paper cites Efros, and Jacob Steinhardt.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Efros, and Jacob Steinhardt

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:18.049846Z

Source-reported events for the cited work

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Observation 3ee5bc23-2402-4cff-861b-ea137071d2f6 · outbound

This paper cites Exact and heuristic algorithms for semi-nonnegative matrix factorization.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Exact and heuristic algorithms for semi-nonnegative matrix factorization

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:18.036018Z

Source-reported events for the cited work

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Observation 748dac07-82d1-4d73-b720-956bb0d2c8ea · outbound

This paper cites Common Data Properties Limit Object-Attribute Binding in CLIP.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Common Data Properties Limit Object-Attribute Binding in CLIP

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:52:17.522066Z

Source-reported events for the cited work

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Observation 66aeee47-b6ee-492f-9710-23f170968b55 · outbound

This paper cites Noise-contrastive estimation: A new estimation principle for unnormalized statistical models.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Noise-contrastive estimation: A new estimation principle for unnormalized statistical models

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:18.021455Z

Source-reported events for the cited work

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Observation 507624f7-8eb0-4b2a-a830-246efd72ea8a · outbound

This paper cites Ridge regression: Biased estimation for nonorthogonal problems.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Ridge regression: Biased estimation for nonorthogonal problems

Reference 19

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Source-reported events for the cited work

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Observation 79d68e4c-8f76-47b3-848e-7e3e1613e346 · outbound

This paper cites Sparse autoencoders find highly interpretable features in language models.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Sparse autoencoders find highly interpretable features in language models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.997887Z

Source-reported events for the cited work

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Observation b34c39fd-6df0-4e81-abc0-5440c759f064 · outbound

This paper cites Huiskes and Michael S.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Huiskes and Michael S

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.984011Z

Source-reported events for the cited work

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Observation e609bdac-67d5-43cc-a6bb-7097669b79f0 · outbound

This paper cites Emergence of phase-and shift-invariant features by decomposition of natural images into independent feature subspaces.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Emergence of phase-and shift-invariant features by decomposition of natural images into independent feature subspaces

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.970095Z

Source-reported events for the cited work

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Observation 66a7e693-8b2b-4de4-9c00-20b2f2c672d3 · outbound

This paper cites Product quantization for nearest neighbor search.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Product quantization for nearest neighbor search

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.956142Z

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Observation 4315b71f-4a77-41c8-8bc7-463fce433b65 · outbound

This paper cites Vo, Patrick Labatut, and Piotr Bojanowski.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Vo, Patrick Labatut, and Piotr Bojanowski

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.939445Z

Source-reported events for the cited work

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Observation 504a600a-63f1-4a77-8549-d32a76db167a · outbound

This paper cites InDiReCT : Language-guided zero-shot deep metric learning for images.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) InDiReCT : Language-guided zero-shot deep metric learning for images

Reference 25

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Observation a869df8f-a864-4ffc-b73b-3ad3ddfc0c0f · outbound

This paper cites Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Mind the gap: Understanding the modality gap in multi-modal contrastive representation learning

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.924261Z

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Observation b32ea399-89ea-42c9-bce9-9bf97597c2a8 · outbound

This paper cites Lawrence Zitnick.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Lawrence Zitnick

Reference 27

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Source-reported events for the cited work

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Observation 39d26f9a-cd00-467a-a832-5c42e627c14e · outbound

This paper cites Image retrieval on real-life images with pre-trained vision-and-language models.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Image retrieval on real-life images with pre-trained vision-and-language models

Reference 28

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Observation ba18ed3d-e919-4328-b598-b83a315d718c · outbound

This paper cites Mallat and Zhifeng Zhang.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Mallat and Zhifeng Zhang

Reference 29

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Observation fde2b633-45fa-4dcc-8e2d-d00fd022d150 · outbound

This paper cites DINOv2: Learning Robust Visual Features without Supervision.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) DINOv2: Learning Robust Visual Features without Supervision

Reference 30

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Observation 99a35309-2ad7-4981-9a38-d9a2990df1fb · outbound

This paper cites Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Orthogonal matching pursuit: Recursive function approximation with applications to wavelet decomposition

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.894387Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) On variational bounds of mutual information

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.880549Z

Source-reported events for the cited work

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Observation 9b5c7e0a-314e-48a2-8c0c-24f12b83a710 · outbound

This paper cites Learning transferable visual models from natural language supervision.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Learning transferable visual models from natural language supervision

Reference 33

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Observation 4e12e4b4-1e2b-4e72-a55b-81b837ef3290 · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) High-resolution image synthesis with latent diffusion models

Reference 34

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Source-reported events for the cited work

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Observation 01a19ac4-4932-4bb9-bb4b-2636bc9a8001 · outbound

This paper cites Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Efficient implementation of the K-SVD algorithm using batch orthogonal matching pursuit

Reference 35

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Observation 179f8c8b-a0cc-438e-bab8-5b2a66fc8a17 · outbound

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Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) A generalized solution of the orthogonal procrustes problem

Reference 36

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Source-reported events for the cited work

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Observation 8588eb5b-216c-449c-90de-57b26ba95722 · outbound

This paper cites Non-negative least squares for high-dimensional linear models: Consistency and sparse recovery without regularization.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Non-negative least squares for high-dimensional linear models: Consistency and sparse recovery without regularization

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.814817Z

Source-reported events for the cited work

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Observation 337ab87f-de8a-4e73-bb90-277355045063 · outbound

This paper cites Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Biomechanical surrogate modelling using stabilized vectorial greedy kernel methods

Reference 38

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Source-reported events for the cited work

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Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Grouped orthogonal matching pursuit for variable selection and prediction

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.800205Z

Source-reported events for the cited work

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Observation ebeb8113-44a9-4362-9a0a-2ef8465f26d1 · outbound

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Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Neural discrete representation learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.785944Z

Source-reported events for the cited work

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Observation f3324f8e-2254-44ce-b865-568dcb7ac948 · outbound

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Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Visualizing data using t-SNE

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.771743Z

Source-reported events for the cited work

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Observation e6a8d3e8-72d8-4f42-8b2e-8aaab1806924 · outbound

This paper cites Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Take and Took, Gaggle and Goose, Book and Read: Evaluating the Utility of Vector Differences for Lexical Relation Learning

Reference 42

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Observation 943a65d6-8eba-49a7-a800-55b88169ecf0 · outbound

This paper cites From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T16:52:17.228046Z

Source-reported events for the cited work

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This paper cites An image is worth 32 tokens for reconstruction and generation.

Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) An image is worth 32 tokens for reconstruction and generation

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:52:17.756768Z

Source-reported events for the cited work

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Observation 3a11778c-e0ea-4c25-93b5-5ac0f789b833 · outbound

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Disentangling Latent Embeddings with Sparse Linear Concept Subspaces (SLiCS) Post-hoc concept bottleneck models

Reference 45

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Unavailable: canonical work link unavailable.

Pith citing papers

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