Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T15:17:57.368308Z
Paper Citation Record · LEDGER
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule
As of 22 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2608.01268.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
21 of 21 outbound references displayed
- verified exact0
- verified fuzzy16
- unresolved5
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation f3cd3110-29f9-4a44-970c-e14d30dde8c8 · outbound
This paper cites Persistence images: A stable vector representation of persistent homology.Journal of Machine Learning Research, 18(8):1–35, 2017.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Persistence images: A stable vector representation of persistent homology.Journal of Machine Learning Research, 18(8):1–35, 2017
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:17:57.295436Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1caf68ae-26e4-42bb-878c-a6b037bfe4ae · outbound
This paper cites DTM-based filtrations.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule DTM-based filtrations
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.591074Z
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 6f213bdf-4b65-449d-8131-dd33b15a4eac · outbound
This paper cites Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Topological Data Analysis for Neural Network Analysis: A Comprehensive Survey
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:17:57.303980Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2f6387de-6fc0-4850-95f5-bb33ee30d7c8 · outbound
This paper cites Statistical topological data analysis using persistence landscapes.Journal of Machine Learning Research, 16(1):77–102, 2015.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Statistical topological data analysis using persistence landscapes.Journal of Machine Learning Research, 16(1):77–102, 2015
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.580699Z
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 6ea60326-93f5-49c9-b0c5-8cd423d5a62c · outbound
This paper cites Efficient and robust persistent homology for measures.Computational Geometry, 58:70–96, 2016.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Efficient and robust persistent homology for measures.Computational Geometry, 58:70–96, 2016
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.570921Z
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 2585e064-27f8-462d-9b7a-987da31e8205 · outbound
This paper cites Topology and data.Bulletin of the American Mathematical Society, 46(2): 255–308, 2009.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Topology and data.Bulletin of the American Mathematical Society, 46(2): 255–308, 2009
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.559708Z
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 729be467-ecbe-4236-9a3b-51da1932ef3a · outbound
This paper cites Gromov-Hausdorff stable signatures for shapes using persistence.Computer Graphics Forum, 28(5):1393–1403, 2009.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Gromov-Hausdorff stable signatures for shapes using persistence.Computer Graphics Forum, 28(5):1393–1403, 2009
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.548583Z
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 703c7094-7a5c-4b8c-8bbd-67a035ecc01f · outbound
This paper cites Geometric inference for proba- bility measures.Foundations of Computational Mathematics, 11(6):733–751, 2011.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Geometric inference for proba- bility measures.Foundations of Computational Mathematics, 11(6):733–751, 2011
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.537828Z
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 d5f88274-a058-4bbb-859a-542a9928cd52 · outbound
This paper cites Stability of persistence diagrams.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Stability of persistence diagrams
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.527651Z
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 4eae4e8b-0d2e-4156-b212-f5bce8402905 · outbound
This paper cites Topological estimation using witness complexes.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Topological estimation using witness complexes
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.517136Z
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 7ff9ad1f-f62c-4926-a586-1ecf8f53be0b · outbound
This paper cites Estimating the intrinsic dimension of datasets by a minimal neighborhood information.Scientific Reports, 7(1):12140, 2017.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Estimating the intrinsic dimension of datasets by a minimal neighborhood information.Scientific Reports, 7(1):12140, 2017
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.507384Z
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 3b996707-6dea-495a-a145-c75cae06066f · outbound
This paper cites Confidence sets for persistence diagrams.The Annals of Statistics, 42(6):2301–2339, 2014.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Confidence sets for persistence diagrams.The Annals of Statistics, 42(6):2301–2339, 2014
Reference 12
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:17:57.336399Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c2735337-1c20-44f3-ad70-55767fee09a0 · outbound
This paper cites Adversary Detection in Neural Networks via Persistent Homology.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Adversary Detection in Neural Networks via Persistent Homology
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:17:57.340004Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 08b356f2-edcb-4f03-9029-82a94ca68c94 · outbound
This paper cites A kernel two-sample test.Journal of Machine Learning Research, 13(25):723–773, 2012.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule A kernel two-sample test.Journal of Machine Learning Research, 13(25):723–773, 2012
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.490862Z
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 0234015c-ce8b-4fc2-bf9d-9472c1074cf1 · outbound
This paper cites Learning deep kernels for non-parametric two-sample tests.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Learning deep kernels for non-parametric two-sample tests
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.480827Z
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 dbc245ab-e247-46b4-a569-d5b663068ef4 · outbound
This paper cites Characterizing adversarial subspaces using lo- cal intrinsic dimensionality.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Characterizing adversarial subspaces using lo- cal intrinsic dimensionality
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.470373Z
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 0d7f8af1-e99d-4863-ad65-86a2e75e162e · outbound
This paper cites Measures of multivariate skewness and kurtosis with applications.Biometrika, 57(3):519–530, 1970.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Measures of multivariate skewness and kurtosis with applications.Biometrika, 57(3):519–530, 1970
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.460323Z
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 4d8c7614-25dc-4927-b66b-ae3ef5eb91e1 · outbound
This paper cites Topology of deep neural networks.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Topology of deep neural networks
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T15:17:57.357995Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9637088a-b973-4cce-991b-b1c2debec2d9 · outbound
This paper cites Failing loudly: An empirical study of methods for detecting dataset shift.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Failing loudly: An empirical study of methods for detecting dataset shift
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.442801Z
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 785f109a-7ebd-44a2-95e7-c089a70de950 · outbound
This paper cites Linear-size approximations to the Vietoris–Rips filtration.Discrete & Computational Geometry, 49(4):778–796, 2013.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Linear-size approximations to the Vietoris–Rips filtration.Discrete & Computational Geometry, 49(4):778–796, 2013
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.431977Z
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 581ef725-b4d3-4f8e-9d2e-88a107ed3c78 · outbound
This paper cites Energy statistics: A class of statistics based on distances.
How fine a change can moments see? A scale law for detecting distribution shift, with a kernel calibration rule Energy statistics: A class of statistics based on distances
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T15:17:57.420551Z
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.