Paper Citation Record · LEDGER

MaxSketch: Robust Distinct Counting in Streams via Random Projections

As of 6 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 1 inbound Pith citation observation for arXiv:2605.15571.

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

pith.paper-citation-record.v1
2605.15571 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-19T20:08:36.821661Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-06T06:34:29.942622+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T15:39:38.848997Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact3
verified fuzzy30
unresolved0
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f4d7dc6f-63e6-4496-8424-a35c0afe526f · outbound

This paper cites Concentration inequalities.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Concentration inequalities

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.874442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 80965443-5cca-4dcc-8cea-8fa0b6f1cf36 · outbound

This paper cites Streaming algorithms for robust distinct elements.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Streaming algorithms for robust distinct elements

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.895064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 86cd8e47-26ad-487f-9d90-519ff780cc8a · outbound

This paper cites Distinct sampling on streaming data with near-duplicates.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Distinct sampling on streaming data with near-duplicates

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.876435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2b590ebb-3566-483e-80c6-725b44a15577 · outbound

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

MaxSketch: Robust Distinct Counting in Streams via Random Projections A simple framework for contrastive learning of visual representations

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.885409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9a100e5f-2fef-4c15-996e-f199d2064b78 · outbound

This paper cites Loglog counting of large cardinalities.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Loglog counting of large cardinalities

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.872481Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation b6b46ca0-dd80-4f80-9c1d-ae3186c47d14 · outbound

This paper cites Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm.Discrete mathematics & theoretical computer science, (Proceedings).

MaxSketch: Robust Distinct Counting in Streams via Random Projections Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm.Discrete mathematics & theoretical computer science, (Proceedings)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.840852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 336b1657-8aeb-41f9-95e6-34d0a38a2113 · outbound

This paper cites Probabilistic counting algorithms for data base applica- tions.Journal of computer and system sciences, 31(2):182–209.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Probabilistic counting algorithms for data base applica- tions.Journal of computer and system sciences, 31(2):182–209

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.863384Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 9e4c6908-0073-4e59-a271-0d070798f5f0 · outbound

This paper cites CNN-based Density Estimation and Crowd Counting: A Survey.

MaxSketch: Robust Distinct Counting in Streams via Random Projections CNN-based Density Estimation and Crowd Counting: A Survey

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:12:44.956457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 57d4e3bb-bd67-4d2c-bcb2-4e00239585b6 · outbound

This paper cites Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Bootstrap your own latent-a new approach to self-supervised learning.Advances in neural information processing systems, 33:21271–21284

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.889808Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 46bfb6ef-6cc0-49c4-accd-dd0ec64b3b23 · outbound

This paper cites Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Neural Collapse Under MSE Loss: Proximity to and Dynamics on the Central Path

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:12:44.962466Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 471ce91f-3111-4afc-80b5-054eec456c15 · outbound

This paper cites Momentum contrast for unsupervised visual representation learning.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Momentum contrast for unsupervised visual representation learning

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.865635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f6dc07d3-4352-4c27-a8ea-efd4dba61bab · outbound

This paper cites Face recognition across pose using view based active appear- ance models (vbaams) on cmu multi-pie dataset.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Face recognition across pose using view based active appear- ance models (vbaams) on cmu multi-pie dataset

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.883380Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8c579f89-68f0-4ac5-9fca-35efafdb89b4 · outbound

This paper cites Hyperloglog in practice: Algorithmic engineering of a state of the art cardinality estimation algorithm.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Hyperloglog in practice: Algorithmic engineering of a state of the art cardinality estimation algorithm

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.881270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 33e6495c-4aee-4447-b5aa-e820a9d0e80f · outbound

This paper cites Long short-term memory.Neural computation, 9(8):1735–1780.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Long short-term memory.Neural computation, 9(8):1735–1780

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.864257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bde82ee5-f2d6-416e-b658-6b39ffd56e25 · outbound

This paper cites Labeled faces in the wild: A database forstudying face recognition in unconstrained environments.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Labeled faces in the wild: A database forstudying face recognition in unconstrained environments

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.858501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5bc1fdf9-1cd7-4a8b-9c7d-e609b907ab36 · outbound

This paper cites Approximate nearest neighbors: towards removing the curse of dimensionality.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Approximate nearest neighbors: towards removing the curse of dimensionality

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.883047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 27bc416f-cea0-4d74-aff2-837d2b4190f6 · outbound

This paper cites Locality-preserving hashing in multidimensional spaces.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Locality-preserving hashing in multidimensional spaces

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.900053Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation fd18ed2b-4eb0-4fd4-99bf-cef4ff6236e2 · outbound

This paper cites An optimal algorithm for the dis- tinct elements problem.

MaxSketch: Robust Distinct Counting in Streams via Random Projections An optimal algorithm for the dis- tinct elements problem

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.878763Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 64a2bd57-93ce-4764-987b-d834296d5eb7 · outbound

This paper cites Generalised f-mean aggregation for graph neural networks.Advances in Neural Information Processing Systems, 36:34439–34450.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Generalised f-mean aggregation for graph neural networks.Advances in Neural Information Processing Systems, 36:34439–34450

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.856021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 45fe012f-35d8-41dc-a09d-535185f419b9 · outbound

This paper cites Springer.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Springer

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.853252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 6615394f-6ccf-45a7-9357-a06cf107d59f · outbound

This paper cites Deep learning face attributes in the wild.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Deep learning face attributes in the wild

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.853583Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 47ed2bef-1f10-4613-846e-b810895d8b8d · outbound

This paper cites Prevalence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences, 117(40):24652–24663.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Prevalence of neural collapse during the terminal phase of deep learning training.Proceedings of the National Academy of Sciences, 117(40):24652–24663

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.860949Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 0d848c68-f671-4899-8f6f-419e6ce05e4f · outbound

This paper cites Learning Aggregation Functions.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Learning Aggregation Functions

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:12:44.959271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation bc53fca1-09da-4df7-93be-9d9459b15c5a · outbound

This paper cites A benchmark of facial recognition pipelines and co-usability performances of modules.Bili¸ sim Teknolojileri Dergisi, 17(2):95–107.

MaxSketch: Robust Distinct Counting in Streams via Random Projections A benchmark of facial recognition pipelines and co-usability performances of modules.Bili¸ sim Teknolojileri Dergisi, 17(2):95–107

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.845390Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 030bf2fd-dcfe-458e-aea2-cfce0d15b09b · outbound

This paper cites Cambridge university press.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Cambridge university press

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.874150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 173ecae3-baca-4b11-a52a-739f735efd33 · outbound

This paper cites The one-sided barrier problem for gaussian noise.Bell System Technical Journal, 41(2):463–501.

MaxSketch: Robust Distinct Counting in Streams via Random Projections The one-sided barrier problem for gaussian noise.Bell System Technical Journal, 41(2):463–501

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.866688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5881da42-018e-4af7-a329-2c216e3a14d1 · outbound

This paper cites On deep set learning and the choice of aggregations.

MaxSketch: Robust Distinct Counting in Streams via Random Projections On deep set learning and the choice of aggregations

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.897569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation d264822e-8fe2-4ae7-b8a2-a1401c8245fb · outbound

This paper cites On the limitations of representing functions on sets.

MaxSketch: Robust Distinct Counting in Streams via Random Projections On the limitations of representing functions on sets

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.880695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation e8f1c429-9f35-4b95-a18b-4824b56c957c · outbound

This paper cites Deep sets.Advances in neural information processing systems, 30.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Deep sets.Advances in neural information processing systems, 30

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.858305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 8d7ec609-b625-4cb7-881c-9da96156cfa6 · outbound

This paper cites Robust statistical analysis on streaming data with near-duplicates in general metric spaces.Proceedings of the ACM on Management of Data, 3(2):1–25.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Robust statistical analysis on streaming data with near-duplicates in general metric spaces.Proceedings of the ACM on Management of Data, 3(2):1–25

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.885586Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 5257e32e-33c8-4388-931b-29c58ed7dce6 · outbound

This paper cites On the optimization landscape of neural collapse under mse loss: Global optimality with unconstrained features.

MaxSketch: Robust Distinct Counting in Streams via Random Projections On the optimization landscape of neural collapse under mse loss: Global optimality with unconstrained features

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.887395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation f5cd9042-493d-46fc-9d65-5cf5e65adc38 · outbound

This paper cites A geometric analysis of neural collapse with unconstrained features.Advances in Neural Information Processing Systems, 34:29820–29834.

MaxSketch: Robust Distinct Counting in Streams via Random Projections A geometric analysis of neural collapse with unconstrained features.Advances in Neural Information Processing Systems, 34:29820–29834

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.890010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 2f2371b9-9a21-4462-9544-95c364e537d3 · outbound

This paper cites Limitations.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Limitations

Reference 33

Resolution: malformed identifier; raw_fallback, observed 2026-05-19T20:13:12.876664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Observation 604119df-6a64-469b-992b-6ece998388d4 · outbound

This paper cites Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects.

MaxSketch: Robust Distinct Counting in Streams via Random Projections Guidelines: • The answer [N/A] means that the paper does not involve crowdsourcing nor research with human subjects

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-19T20:13:12.892706Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

Pith citing papers

Observation 84f8844c-f519-4b9e-be37-78383e2a7cdf · inbound

Mergeable Model-Side Aggregation States for Long-Context Language Models cites this paper.

Mergeable Model-Side Aggregation States for Long-Context Language Models MaxSketch: Robust Distinct Counting in Streams via Random Projections

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-01T15:39:38.848997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.