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Paper Citation Record · LEDGER

Deep Clustering via Probabilistic Ratio-Cut Optimization

As of 12 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 1 inbound Pith citation observation for arXiv:2502.03405.

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

pith.paper-citation-record.v1
2502.03405 v2

Coverage vector

measured 35 of 35 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T05:02:45.189500Z

measured 36 of 36 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:47:51.317948Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-04T22:47:54.806558Z

Reference resolution

35 of 35 outbound references displayed

  • verified exact3
  • verified fuzzy19
  • unresolved13
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 57aefe52-877a-4437-b3ed-586329a0ca86 · outbound

This paper cites Alam and S.

Deep Clustering via Probabilistic Ratio-Cut Optimization Alam and S

Reference 1

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-09T05:02:44.471759Z digest=sha256:e1894d668b0d1341fe597c0fd60c8e12d1c8943b47fe791b4ec033b52ade52fe

Observation 6e505b99-86ff-4f31-ab92-d1367e5ee36b · outbound

This paper cites Iterative bregman projections for regularized transportation problems, 2014.

Deep Clustering via Probabilistic Ratio-Cut Optimization Iterative bregman projections for regularized transportation problems, 2014

Reference 2

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 5bfc8ca7-4154-469f-86e7-9c53861299b4 · outbound

This paper cites Chen and Jun S.

Deep Clustering via Probabilistic Ratio-Cut Optimization Chen and Jun S

Reference 3

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation d7d8e74b-8616-4618-8a1c-3a63e2b8de70 · outbound

This paper cites A simple framework for contrastive learning of visual representations, 2020.

Deep Clustering via Probabilistic Ratio-Cut Optimization A simple framework for contrastive learning of visual representations, 2020

Reference 4

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T05:02:44.677910Z digest=sha256:1e0c610e23edec513ccfaae40c95ffce744aa74aea8db1ab931c63be8f0ac36c

Observation 6673d308-e282-43eb-8a89-38a4fbf6af75 · outbound

This paper cites solo-learn: A library of self-supervised methods for visual representation learning.

Deep Clustering via Probabilistic Ratio-Cut Optimization solo-learn: A library of self-supervised methods for visual representation learning

Reference 5

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-09T05:02:44.738668Z digest=sha256:285a71f39224f461eb7d2b15580d3f11575f33de1e93a09c609d48cfca8782cc

Observation 27c34d30-0ab4-4cd3-bdad-71e959872090 · outbound

This paper cites Dhillon, Yuqiang Guan, and Brian Kulis.

Deep Clustering via Probabilistic Ratio-Cut Optimization Dhillon, Yuqiang Guan, and Brian Kulis

Reference 6

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-09T05:02:44.797117Z digest=sha256:1858a6b8d9aa8a6097e325e2279f025443ea601e1a69fe5e70a01843d3025bc8

Observation 82d38c13-bf5c-4959-b575-2f42c8dc18b4 · outbound

This paper cites Estepa, Ignacio Sarasua, Bhalaji Nagarajan, and Petia Radeva.

Deep Clustering via Probabilistic Ratio-Cut Optimization Estepa, Ignacio Sarasua, Bhalaji Nagarajan, and Petia Radeva

Reference 7

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 09825bf2-94c7-4819-a23a-39328a9fa566 · outbound

This paper cites Ezugwu, Abiodun M.

Deep Clustering via Probabilistic Ratio-Cut Optimization Ezugwu, Abiodun M

Reference 8

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 508a1ac2-6aa6-4b2c-959b-3aa6ca2c7621 · outbound

This paper cites Let go of your labels with unsupervised transfer, 2024.

Deep Clustering via Probabilistic Ratio-Cut Optimization Let go of your labels with unsupervised transfer, 2024

Reference 9

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 120bf13b-db7d-4819-8df7-66641bdc2424 · outbound

This paper cites an unresolved cited work.

Deep Clustering via Probabilistic Ratio-Cut Optimization Unresolved cited work

Reference 10

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 26ae2359-3443-4085-bb4b-69ff8b0914a4 · outbound

This paper cites Hagen and Andrew B.

Deep Clustering via Probabilistic Ratio-Cut Optimization Hagen and Andrew B

Reference 11

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 0946d5a8-dbf4-490c-a5be-a051e2e86a90 · outbound

This paper cites Bulirsch J.

Deep Clustering via Probabilistic Ratio-Cut Optimization Bulirsch J

Reference 12

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation f9dce997-8048-467a-95a4-82139bf7aab3 · outbound

This paper cites A decoder-free variational deep embedding for unsupervised clustering.

Deep Clustering via Probabilistic Ratio-Cut Optimization A decoder-free variational deep embedding for unsupervised clustering

Reference 13

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 9a2b3a98-8275-411d-88da-fe8384db713c · outbound

This paper cites Variational deep embedding: An unsupervised and generative approach to clustering, 2017.

Deep Clustering via Probabilistic Ratio-Cut Optimization Variational deep embedding: An unsupervised and generative approach to clustering, 2017

Reference 14

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-09T05:02:44.990450Z digest=sha256:7deb6adea76a749197c78544126bd6f5fdc680e09a7e97db2f8190a5a7d15c8a

Observation 156589ff-a35a-49d7-99e0-41345f9e26fd · outbound

This paper cites Learning multiple layers of features from tiny images, 2009.

Deep Clustering via Probabilistic Ratio-Cut Optimization Learning multiple layers of features from tiny images, 2009

Reference 15

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T05:02:44.995275Z digest=sha256:ef410501e01215a96787dee910f731a70a83e6236b5474f03e8b5041cb809f69

Observation a6124d8b-a2b8-406f-a5fc-5a48cfccaa19 · outbound

This paper cites Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation.

Deep Clustering via Probabilistic Ratio-Cut Optimization Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation

Reference 16

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

source=arxiv_source observed=2026-08-09T05:02:45.000163Z digest=sha256:eaeeeec1d5c8c9a23bfd388c8443092c17b03ac1a023b5409d5c1fede0e5881e

Observation 0c6b7bb9-11ab-4064-9d09-b5403d438f61 · outbound

This paper cites Lecun, L.

Deep Clustering via Probabilistic Ratio-Cut Optimization Lecun, L

Reference 17

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no resolver link, observed 2026-08-09T05:02:45.005432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T05:02:45.005432Z digest=sha256:1507efac51766c07a262951678da068fe7142332103039d778f88535cf55bc75

Observation eca909a1-ea4b-4ebb-af8f-7622427fbe10 · outbound

This paper cites Contrastive Clustering.

Deep Clustering via Probabilistic Ratio-Cut Optimization Contrastive Clustering

Reference 18

Resolution
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no resolver link, observed 2026-08-09T05:02:45.011107Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation a0ff9741-9b91-4a80-a04d-76c6a0072bbf · outbound

This paper cites an unresolved cited work.

Deep Clustering via Probabilistic Ratio-Cut Optimization Unresolved cited work

Reference 19

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation bd1817cd-8805-479c-8b03-5e75d8e6c899 · outbound

This paper cites Machado, Marc G.

Deep Clustering via Probabilistic Ratio-Cut Optimization Machado, Marc G

Reference 20

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 852dff9b-dd99-4e86-9f72-f7f95bd03bfb · outbound

This paper cites Eigenoption Discovery through the Deep Successor Representation.

Deep Clustering via Probabilistic Ratio-Cut Optimization Eigenoption Discovery through the Deep Successor Representation

Reference 21

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 9dbdebce-a06a-4770-9e36-1415c024b926 · outbound

This paper cites Exploiting sparsity to improve the accuracy of nyström-based large-scale spectral clustering.

Deep Clustering via Probabilistic Ratio-Cut Optimization Exploiting sparsity to improve the accuracy of nyström-based large-scale spectral clustering

Reference 22

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation c02f547d-7767-4905-b0e5-c7daafb231af · outbound

This paper cites Algorithms for the assignment and transportation problems.

Deep Clustering via Probabilistic Ratio-Cut Optimization Algorithms for the assignment and transportation problems

Reference 23

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 2d0289a4-e345-4be9-a2aa-312d32c08298 · outbound

This paper cites On spectral clustering: Analysis and an algorithm.

Deep Clustering via Probabilistic Ratio-Cut Optimization On spectral clustering: Analysis and an algorithm

Reference 24

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 2ad691aa-112e-487b-98dc-4578ca812660 · outbound

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

Deep Clustering via Probabilistic Ratio-Cut Optimization DINOv2: Learning Robust Visual Features without Supervision

Reference 25

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation f6bda4b5-5d5d-4c6e-b4c1-14780da2ff59 · outbound

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Deep Clustering via Probabilistic Ratio-Cut Optimization Unresolved cited work

Reference 26

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unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 1b2c4e82-7839-4f7f-8302-a1292e47dc09 · outbound

This paper cites an unresolved cited work.

Deep Clustering via Probabilistic Ratio-Cut Optimization Unresolved cited work

Reference 27

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 67dcd75a-1b8c-45c8-8890-3177723abc36 · outbound

This paper cites Learning Transferable Visual Models From Natural Language Supervision.

Deep Clustering via Probabilistic Ratio-Cut Optimization Learning Transferable Visual Models From Natural Language Supervision

Reference 28

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8871022b-55cc-4c4a-857c-e8c888a088a4 · outbound

This paper cites Spectralnet: Spectral clustering using deep neural networks.

Deep Clustering via Probabilistic Ratio-Cut Optimization Spectralnet: Spectral clustering using deep neural networks

Reference 29

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 6ab9275e-a4c7-4283-91bd-6785022720b6 · outbound

This paper cites Fast and accurate k-means for large datasets.

Deep Clustering via Probabilistic Ratio-Cut Optimization Fast and accurate k-means for large datasets

Reference 30

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 0f35be2a-f377-4645-be9a-5462a0c10776 · outbound

This paper cites The VampPrior Mixture Model.

Deep Clustering via Probabilistic Ratio-Cut Optimization The VampPrior Mixture Model

Reference 31

Resolution
verified exact
local_arxiv, observed 2026-08-09T05:02:45.293274Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 4b2f9328-5b2a-40fa-b301-7bcaff23a297 · outbound

This paper cites A Tutorial on Spectral Clustering.

Deep Clustering via Probabilistic Ratio-Cut Optimization A Tutorial on Spectral Clustering

Reference 32

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3312ad3f-a1f9-4402-a398-1d8062455da1 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Deep Clustering via Probabilistic Ratio-Cut Optimization Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 33

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 8d94c760-ec8b-4b74-855b-4b92dfe240e9 · outbound

This paper cites Streaming spectral clustering.

Deep Clustering via Probabilistic Ratio-Cut Optimization Streaming spectral clustering

Reference 34

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation 6659e160-7e02-459c-a5d9-8f61a5048d2a · outbound

This paper cites write newline.

Deep Clustering via Probabilistic Ratio-Cut Optimization write newline

Reference 35

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T05:02:45.189500Z digest=sha256:b8f753d7364ed0ee9823fd6b178ebbc9cb9bb0c720a1e5daf006635eb18f5511

Pith citing papers

Observation 84fe10ac-d6fa-4205-832f-3c4003723240 · inbound

Dimensionally Reduced Open-World Clustering: DROWCULA cites this paper.

Dimensionally Reduced Open-World Clustering: DROWCULA Deep Clustering via Probabilistic Ratio-Cut Optimization

Reference 20

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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