Paper Citation Record · LEDGER

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation

As of 10 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2509.24467.

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

pith.paper-citation-record.v1
2509.24467 v4

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T14:43:35.407031Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

15 of 15 outbound references displayed

verified exact0
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b785f2c4-0d07-4326-aab1-9f924094c720 · outbound

This paper cites Julien Mairal.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Julien Mairal

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:34.794741Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 134ed84e-81f1-497b-b8db-a4b31a221ed0 · outbound

This paper cites Neural Kernels Without Tangents.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Neural Kernels Without Tangents

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:35.112926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7ee8cab8-3660-445c-84fb-c62ec37e2ed6 · outbound

This paper cites Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and St´ephane Deny.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Jure Zbontar, Li Jing, Ishan Misra, Yann LeCun, and St´ephane Deny

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:35.283400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 134e9112-34c0-47ea-8b85-8224a4ac525e · outbound

This paper cites Loss Function Hyper-parametersFor the simple contrastive loss, spectral contrastive loss, BT, and KPCA, the regularization coefficientλis sampled from the range[10 −5,100].

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Loss Function Hyper-parametersFor the simple contrastive loss, spectral contrastive loss, BT, and KPCA, the regularization coefficientλis sampled from the range[10 −5,100]

Reference 256

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:35.407031Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65e92e69-8293-40eb-a0f2-e26e2fa70d15 · outbound

This paper cites Towards A Rigorous Science of Interpretable Machine Learning.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Towards A Rigorous Science of Interpretable Machine Learning

Reference 1411

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:33.646768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 343d20f6-0a31-495b-bf4f-08c9df48c859 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Adam: A Method for Stochastic Optimization

Reference 1989

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:34.491636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6068096f-5dd1-406d-98f2-2af3b42d94cb · outbound

This paper cites Elias Frantar, Eldar Kurtic, and Dan Alistarh.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Elias Frantar, Eldar Kurtic, and Dan Alistarh

Reference 2008

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:34.044749Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eb1d89f7-63c5-47d0-ad0b-3961a1eb7582 · outbound

This paper cites URLhttps://doi.org/10.1137/090771806.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation URLhttps://doi.org/10.1137/090771806

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:34.372399Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 317ac1fa-66e5-461c-80eb-5899dda8a0e5 · outbound

This paper cites Neural Tangents: Fast and Easy Infinite Neural Networks in Python.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Neural Tangents: Fast and Easy Infinite Neural Networks in Python

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:35.015653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c595462a-1b9b-415e-8c28-b73bf79093a8 · outbound

This paper cites Julien Mairal, Piotr Koniusz, Zaid Harchaoui, and Cordelia Schmid.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Julien Mairal, Piotr Koniusz, Zaid Harchaoui, and Cordelia Schmid

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:34.904739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 56028b67-6b10-4b86-870f-d5697ce2bf64 · outbound

This paper cites Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, and Andrea Montanari.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Behrooz Ghorbani, Song Mei, Theodor Misiakiewicz, and Andrea Montanari

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:34.173799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1991a402-0b6b-4b45-9474-b445cd7f02ac · outbound

This paper cites David Arthur and Sergei Vassilvitskii.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation David Arthur and Sergei Vassilvitskii

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:33.527837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd28fe45-4b02-4ee8-9d4d-ff6d0410d78d · outbound

This paper cites Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:34.687216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8bc046b0-63f7-4b93-be77-c0ea5ee41db4 · outbound

This paper cites Pascal Mattia Esser, Maximilian Fleissner, and Debarghya Ghoshdastidar.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Pascal Mattia Esser, Maximilian Fleissner, and Debarghya Ghoshdastidar

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:33.814744Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 616a8a4b-2e3f-441d-a678-102e6e2980a6 · outbound

This paper cites Alexander Wei, Wei Hu, and Jacob Steinhardt.

Interpretable Self-Supervised Learning via Representer Landmarks and Nystr\"om Approximation Alexander Wei, Wei Hu, and Jacob Steinhardt

Reference 2195

Resolution: unresolved; no resolver link, observed 2026-08-04T14:43:35.206630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.