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Paper Citation Record · LEDGER

Representation learning with a transformer by contrastive learning for money laundering detection

As of 16 August 2026, this Paper Citation Record lists 65 of 65 outbound references and 0 inbound Pith citation observations for arXiv:2507.08835.

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

pith.paper-citation-record.v1
2507.08835 v1

Coverage vector

measured 65 of 65 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:48:47.606453Z

measured 65 of 65 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

65 of 65 outbound references displayed

  • verified exact0
  • verified fuzzy51
  • unresolved14
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9783ac01-5ce3-4d78-9a22-8d32431f6d2d · outbound

This paper cites Mount, Nathan S.

Representation learning with a transformer by contrastive learning for money laundering detection Mount, Nathan S

Reference 1

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 30309462-9587-4b1c-a020-e2fb45ae4149 · outbound

This paper cites Controlling the false discovery rate: A practical and powerful approach to multiple testing.

Representation learning with a transformer by contrastive learning for money laundering detection Controlling the false discovery rate: A practical and powerful approach to multiple testing

Reference 2

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:39.629147Z digest=sha256:33926dbbc32b35200ee1a873ce029025e86d445858b20013c5bdb5c56b1d1f0d

Observation 86771735-2278-404c-b51e-1a27c8394914 · outbound

This paper cites an unresolved cited work.

Representation learning with a transformer by contrastive learning for money laundering detection Unresolved cited work

Reference 3

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation b5f7b431-10cd-4c14-9a48-3f9db2a20fb1 · outbound

This paper cites Rule-based anti-money laundering in financial intelligence units: Experience and vision.

Representation learning with a transformer by contrastive learning for money laundering detection Rule-based anti-money laundering in financial intelligence units: Experience and vision

Reference 4

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:39.891124Z digest=sha256:671645a0ccf31aa12c69906af537fb7396dacb8ece825802abaa19ef4b90d224

Observation a00ddda0-f5dd-4654-98db-bd4a4abbf86a · outbound

This paper cites Large-scale machine learning with stochastic gradient descent.

Representation learning with a transformer by contrastive learning for money laundering detection Large-scale machine learning with stochastic gradient descent

Reference 5

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:40.039505Z digest=sha256:ff7bcd5380e5be93071838cdccdba1570989e053f4c75596e6774b23281ddfc8

Observation 72937a5a-9e79-4ecf-b3cd-988de0d8a3d1 · outbound

This paper cites Convex Optimization.

Representation learning with a transformer by contrastive learning for money laundering detection Convex Optimization

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 2026-08-15T06:32:42.880941+00:00.

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Observation a595c748-b7f4-42bc-81c8-cc936e6d5fbe · outbound

This paper cites The control of the false discovery rate in multiple testing under dependency.

Representation learning with a transformer by contrastive learning for money laundering detection The control of the false discovery rate in multiple testing under dependency

Reference 7

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:40.311705Z digest=sha256:9d1cf7e9255bf11db84f978e80db6f3fac5a6a6767a3d61e331534fdb8d972ea

Observation e87cb87e-2b5a-4cfb-8945-ce0c6a4ddc8a · outbound

This paper cites Xgboost: A scalable tree boosting system.

Representation learning with a transformer by contrastive learning for money laundering detection Xgboost: A scalable tree boosting system

Reference 8

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:40.435675Z digest=sha256:415ced273af3b81a0b404794d45298c49b2ad0f5f522fd8eab0c9b74acea0ba2

Observation 1d1dcc87-eed2-4ddf-a19b-924519dde944 · outbound

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

Representation learning with a transformer by contrastive learning for money laundering detection A simple framework for contrastive learning of visual representations, 2020

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:48:40.555777Z digest=sha256:415477c5056f29910b335646586e526ac90e644846b2e3fad8013c2ef4a1959b

Observation 03c1b3c8-7e4a-434e-a542-ceb3ddd4776f · outbound

This paper cites A cross-validation based estimation of the proportion of true null hypotheses.

Representation learning with a transformer by contrastive learning for money laundering detection A cross-validation based estimation of the proportion of true null hypotheses

Reference 10

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:40.681031Z digest=sha256:8a3d1ef10c98d16ca94c51ba4b58fcfc17b501377a845ee90d4e8f07b72d831a

Observation fa48a75a-0c22-4c4a-9ddf-e5d7e3769737 · outbound

This paper cites Bert: Pre-training of deep bidirectional transformers for language understanding.

Representation learning with a transformer by contrastive learning for money laundering detection Bert: Pre-training of deep bidirectional transformers for language understanding

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:58.287405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:40.804951Z digest=sha256:feb8ff9f38d502d2cf071108922eb3494bf8ed9aeece379964becddc239406ad

Observation 780357b8-269d-43cb-ad7e-d82c0af98df9 · outbound

This paper cites Learning deep representations using convolutional auto-encoders with symmetric skip connections, 2017.

Representation learning with a transformer by contrastive learning for money laundering detection Learning deep representations using convolutional auto-encoders with symmetric skip connections, 2017

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:40.951584Z digest=sha256:0614cef9a4a27907d94fa15c5e90e73a17197c0e5137332bb6d02992e84792e0

Observation c52c908c-6d60-4563-bdeb-f79d074716ef · outbound

This paper cites Position information in transformers: An overview, 2021.

Representation learning with a transformer by contrastive learning for money laundering detection Position information in transformers: An overview, 2021

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:41.063338Z digest=sha256:abc4e2877ec55e7496aecf020f0769b2ad78c5a201bb59187e7a7c5c3ccf9197

Observation e846a261-59f4-49fe-b2c9-1ec45118c9d8 · outbound

This paper cites an unresolved cited work.

Representation learning with a transformer by contrastive learning for money laundering detection Unresolved cited work

Reference 14

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:41.193882Z digest=sha256:1041e6b41a3522ce8d77362fadeb2a1cf15d7f86a44f84994f4678deed1fa5c2

Observation 34aac246-bac2-4273-bee8-3f31aa4d33ab · outbound

This paper cites de Almeida, Hassan Sirelkhatim, Guillaume Richard, Marcin Skwark, Karim Beguir, Marie Lopez, and Thomas Pierrot.

Representation learning with a transformer by contrastive learning for money laundering detection de Almeida, Hassan Sirelkhatim, Guillaume Richard, Marcin Skwark, Karim Beguir, Marie Lopez, and Thomas Pierrot

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:57.267740Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:41.280423Z digest=sha256:ad456d64f78f3f4ae7e440826d64042221f38e93cb6fe03ac8e8892c0eb0b3da

Observation 966f4307-c60a-4681-932a-eaeb5faba11d · outbound

This paper cites Anti-money laundering alert optimization using machine learning with graphs, 2022.

Representation learning with a transformer by contrastive learning for money laundering detection Anti-money laundering alert optimization using machine learning with graphs, 2022

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:41.415433Z digest=sha256:8642a9e165129392fc8ca9ed2479d63064a86c3262c341c40c2e50f5f4e06ede

Observation 33d96fcd-f362-4366-ad1f-0678bec62df7 · outbound

This paper cites an unresolved cited work.

Representation learning with a transformer by contrastive learning for money laundering detection Unresolved cited work

Reference 17

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:41.567525Z digest=sha256:b0737e04bd8bc8d60c2aae22ed66b9305e5b431122e0a508386d6d71630e4cad

Observation 858ba5db-3b82-463f-b064-52efc631aaf2 · outbound

This paper cites an unresolved cited work.

Representation learning with a transformer by contrastive learning for money laundering detection Unresolved cited work

Reference 18

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 31aa3a20-20c4-47dd-8d89-697fe812b192 · outbound

This paper cites Understanding and Improving the Role of Projection Head in Self-Supervised Learning.

Representation learning with a transformer by contrastive learning for money laundering detection Understanding and Improving the Role of Projection Head in Self-Supervised Learning

Reference 19

Resolution
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:48:41.800883Z digest=sha256:d301b712a06fc5d16fd80777744cd11e5451c5f6bfbc83481cce6f71f15146f8

Observation dbc4d822-dab5-41bd-b227-02bf9d6ad3fb · outbound

This paper cites Understanding and improving the role of projection head in self-supervised learning, 2022.

Representation learning with a transformer by contrastive learning for money laundering detection Understanding and improving the role of projection head in self-supervised learning, 2022

Reference 20

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 475e9c26-5491-480a-9b7c-62660ca19821 · outbound

This paper cites Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank, 2023.

Representation learning with a transformer by contrastive learning for money laundering detection Rankme: Assessing the downstream performance of pretrained self-supervised representations by their rank, 2023

Reference 21

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

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Observation 1c8dc7ad-f872-4ad2-a23c-8ab8bb55cf54 · outbound

This paper cites A survey on self-supervised learning: Algorithms, applications, and future trends.

Representation learning with a transformer by contrastive learning for money laundering detection A survey on self-supervised learning: Algorithms, applications, and future trends

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 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:42.158340Z digest=sha256:766044f22b3477367186d55056677e1ae0879cb37e7104769b1586a406b4256e

Observation c66f78eb-8576-4567-b69f-98ead1c3f835 · outbound

This paper cites Diffprivlib: The IBM Differential Privacy Library.

Representation learning with a transformer by contrastive learning for money laundering detection Diffprivlib: The IBM Differential Privacy Library

Reference 23

Resolution
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no resolver link, observed 2026-08-06T19:48:42.276300Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:48:42.276300Z digest=sha256:0ea21307afd3a6173ac601d18c7d18d77c269b77a41bae08db3ff3bb232b489f

Observation 4dcae69c-bd17-40a9-ad2d-8732854af72c · outbound

This paper cites Momentum contrast for unsupervised visual representation learning, 2020.

Representation learning with a transformer by contrastive learning for money laundering detection Momentum contrast for unsupervised visual representation learning, 2020

Reference 24

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unresolved
no resolver link, observed 2026-08-06T19:48:42.410082Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:48:42.410082Z digest=sha256:6100bb269d749f4d4308783fe28121f8dcc09eb28b300d4db73fd35d8f12eb72

Observation 9bbe9324-3de0-46b9-952c-bb44f7013918 · outbound

This paper cites Walter, Michael Maire, and Maryam Khademi.

Representation learning with a transformer by contrastive learning for money laundering detection Walter, Michael Maire, and Maryam Khademi

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:55.676458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:42.527589Z digest=sha256:a670fe2bacc83681431ee27d73e6ba44fca41adb1b85e511ae1088d7bbddc6c3

Observation 75774f83-d3e0-42ce-89e5-3ea763367fec · outbound

This paper cites Universal language model fine-tuning for text classification, 2018.

Representation learning with a transformer by contrastive learning for money laundering detection Universal language model fine-tuning for text classification, 2018

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T19:48:42.659801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:48:42.659801Z digest=sha256:d9edd3287450ad5811ca81e9d175d253991e586b1b2c589df43f10d84585ecce

Observation a7c67389-f608-4313-bf04-0655f8796314 · outbound

This paper cites A comprehensive survey on contrastive learning.

Representation learning with a transformer by contrastive learning for money laundering detection A comprehensive survey on contrastive learning

Reference 27

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:55.408464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:42.770307Z digest=sha256:3b7653ffb1d3b043dc12bc972b86514160a4ec56cdba9b181ee36f4d5f08ed46

Observation efcd6555-63ac-45a3-ac04-c1e5b72e0527 · outbound

This paper cites Design of a monitor for detecting money laundering and terrorist financing.

Representation learning with a transformer by contrastive learning for money laundering detection Design of a monitor for detecting money laundering and terrorist financing

Reference 28

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:55.184547Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:42.877073Z digest=sha256:df9bcf961672a50a107b436de3c11a63f3ea5372f50d0e18555d76da86c50098

Observation c9d78c10-c5b8-4bdc-914a-eefff69c4d30 · outbound

This paper cites Ikotun, Absalom E.

Representation learning with a transformer by contrastive learning for money laundering detection Ikotun, Absalom E

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:54.944612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:43.011059Z digest=sha256:7f8d3525b4183f9acebecfe03c67b6d9646495446e6fe13800d50749a2690537

Observation 6be5e1ce-06aa-42a2-843b-10da1d9b957d · outbound

This paper cites an unresolved cited work.

Representation learning with a transformer by contrastive learning for money laundering detection Unresolved cited work

Reference 30

Resolution
unresolved
raw_fallback, observed 2026-08-06T19:48:54.652978Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:43.163732Z digest=sha256:bae94c8be4dbcb0ab66361b4bd56ac22713662e9720ec384cac46ee6f34199af

Observation 7aea823b-b837-42c1-8c6f-dfb743b20657 · outbound

This paper cites Augmenting imbalanced time-series data via adversarial perturbation in latent space.

Representation learning with a transformer by contrastive learning for money laundering detection Augmenting imbalanced time-series data via adversarial perturbation in latent space

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:54.503437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:43.290715Z digest=sha256:57e019ee665847576c392fb995c5c876c76682e98b0bd685faf11ce82ba7f1d2

Observation a2784c92-e1db-498b-b848-610af5517cc9 · outbound

This paper cites Kim, Alejandro Cosa-Linan, Nandhini Santhanam, Mahboubeh Jannesari, Mate E.

Representation learning with a transformer by contrastive learning for money laundering detection Kim, Alejandro Cosa-Linan, Nandhini Santhanam, Mahboubeh Jannesari, Mate E

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:54.309946Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:43.416080Z digest=sha256:c0bfb246f4d2fe61d1dfcabef9ebabc3a7eda97beeb7966359d6755581be85af

Observation b3f3d355-5136-4e4e-847a-21f2d8df02c1 · outbound

This paper cites Portfolio transformer for attention-based asset allocation, 2022.

Representation learning with a transformer by contrastive learning for money laundering detection Portfolio transformer for attention-based asset allocation, 2022

Reference 33

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:54.159897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:43.512345Z digest=sha256:bdd3d577816ce771f494db45a0c5931c7566eca004340fa7aee1ce007fee19a0

Observation 0bae4268-e55f-4c47-819b-ffa166346705 · outbound

This paper cites Deep learning and explainable artificial intelligence techniques applied for detecting money laundering–a critical review.

Representation learning with a transformer by contrastive learning for money laundering detection Deep learning and explainable artificial intelligence techniques applied for detecting money laundering–a critical review

Reference 34

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:53.983773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:43.632862Z digest=sha256:69213ff4edd4d1e942f886c7254f872535cc941aae4c4a2b4708ec989414b6c0

Observation db101b05-dc5d-4315-8f6e-37b0067b4f9a · outbound

This paper cites Supervised contrastive learning, 2021.

Representation learning with a transformer by contrastive learning for money laundering detection Supervised contrastive learning, 2021

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-06T19:48:43.757305Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:48:43.757305Z digest=sha256:2f886e386e13d7a92738e6f03e44cde0dc9880a75e4a62ff6929fab3a95dea0a

Observation 7ffee8bf-1e51-4410-bc73-b022cfff1d93 · outbound

This paper cites Temporal ensembling for semi-supervised learning, 2017.

Representation learning with a transformer by contrastive learning for money laundering detection Temporal ensembling for semi-supervised learning, 2017

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:53.843844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:43.893126Z digest=sha256:cb4adad79f77cda56d1a729346ef58331ee42ab68e943ddaff78a1a716885ed0

Observation e32194b1-527b-47ec-a7f0-fc21ffff74d9 · outbound

This paper cites HaoChen, Adrien Gaidon, and Tengyu Ma.

Representation learning with a transformer by contrastive learning for money laundering detection HaoChen, Adrien Gaidon, and Tengyu Ma

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-06T19:48:44.029372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:48:44.029372Z digest=sha256:a877695700f487e3160079580b16b626cf8577582eec867063b4a5ae695bd804

Observation 237b6144-2b47-43d9-8f75-7d9131623e23 · outbound

This paper cites Le-Khac, Graham Healy, and Alan F.

Representation learning with a transformer by contrastive learning for money laundering detection Le-Khac, Graham Healy, and Alan F

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:53.573852Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:44.130503Z digest=sha256:12b74e8c48cc43a33dd67ebd2d7c6ab2b341be019737c9110c79f3ea8c9b6b56

Observation a95208a8-e5d3-4a8a-8477-77c554e0de2f · outbound

This paper cites Anti-money laundering in the eu: Time to get serious.

Representation learning with a transformer by contrastive learning for money laundering detection Anti-money laundering in the eu: Time to get serious

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:53.366730Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:44.233715Z digest=sha256:772049ccae8f1ee6de3a8acc560beb49891f8c406363ef1d679942e26e199bbe

Observation f6b4d5e9-5ceb-47de-ba4b-8e0eadcbe6c4 · outbound

This paper cites Financial Fraud and Cybercrime in Wartime: An Overview of the Scientific Landscape and Insights from Countries Engaged in Military Conflict.

Representation learning with a transformer by contrastive learning for money laundering detection Financial Fraud and Cybercrime in Wartime: An Overview of the Scientific Landscape and Insights from Countries Engaged in Military Conflict

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:53.116616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:44.348795Z digest=sha256:859ae69c578f514b760a5a9cb94d8691ded4e47fed4fa5718f24474d9720c5d5

Observation cc2551a2-4486-4a4a-9d3c-cbe329036371 · outbound

This paper cites A preprocessing scheme for high‐cardinality categorical attributes in classification and prediction problems.

Representation learning with a transformer by contrastive learning for money laundering detection A preprocessing scheme for high‐cardinality categorical attributes in classification and prediction problems

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:52.895980Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:44.447083Z digest=sha256:af15d69a4040d6c663b9fe02dbb47d6bbb4faa91a942cec4c08565bb7524e982

Observation 05fca37d-baef-4fe6-9b75-f570519e26e0 · outbound

This paper cites Cross-entropy loss functions: Theoretical analysis and applications, 2023.

Representation learning with a transformer by contrastive learning for money laundering detection Cross-entropy loss functions: Theoretical analysis and applications, 2023

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:52.705075Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:44.546295Z digest=sha256:64bb616b09061a16982e18e594ba9e1b41aa8975b98d3554814a98094d73f2ba

Observation 8024a808-b8ce-4c37-872b-725f4e428c1e · outbound

This paper cites Money laundering, proceeds of crime and the financing of terrorism.

Representation learning with a transformer by contrastive learning for money laundering detection Money laundering, proceeds of crime and the financing of terrorism

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:52.533091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:44.643501Z digest=sha256:e642b0c71d5a9eebf6cf8ffaf92c9c4f6ce25206b8b8fe453eb27806d245850a

Observation 458e7155-35a5-42ff-a58e-a9d294df7fc0 · outbound

This paper cites Regularizing deep neural networks by noise: Its interpretation and optimization.

Representation learning with a transformer by contrastive learning for money laundering detection Regularizing deep neural networks by noise: Its interpretation and optimization

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:52.339995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:44.757012Z digest=sha256:6461c607b0c62106b9887a53543965e11c961e53944853fe3ecabb39cda202b8

Observation 6d8777a9-5e55-4a66-90e9-fa5703a14c35 · outbound

This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks, 2019.

Representation learning with a transformer by contrastive learning for money laundering detection Sentence-bert: Sentence embeddings using siamese bert-networks, 2019

Reference 45

Resolution
unresolved
no resolver link, observed 2026-08-06T19:48:44.851369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:48:44.851369Z digest=sha256:29bef93980a05f95b18b334f5002121f70d151b6b7f9de294ba16a1e739e26ae

Observation 4eb54757-b823-4ac0-975e-c45b52404496 · outbound

This paper cites Williams.

Representation learning with a transformer by contrastive learning for money laundering detection Williams

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:52.061636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:44.997698Z digest=sha256:4a1c0a028ac474516e2b0510a91e8b093d8904db8dd9b6c0ecdf5443c4f26b0d

Observation 0ee2abd4-e1ae-441d-8109-524a72cd27a6 · outbound

This paper cites Improving language understanding by generative pre-training.

Representation learning with a transformer by contrastive learning for money laundering detection Improving language understanding by generative pre-training

Reference 47

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:51.708863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:45.132643Z digest=sha256:f11a43d152ca1a42c374d9a13361db51f522def0de7fbf0a9d971de3bfb4d8e5

Observation 470793f0-9ba1-4cd8-bce9-2bfe49c7863a · outbound

This paper cites Rousseeuw.

Representation learning with a transformer by contrastive learning for money laundering detection Rousseeuw

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:51.356161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:45.242944Z digest=sha256:bfaf00758df6284ca4161503d1905ba8ee6b3c2a99f54c9f11c98a58f4edaf83

Observation 601406f9-3dd6-4647-adae-b476bc84a29a · outbound

This paper cites A natural language processing approach for financial fraud detection.

Representation learning with a transformer by contrastive learning for money laundering detection A natural language processing approach for financial fraud detection

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:51.183745Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:45.362333Z digest=sha256:545ffabfeced4247c4acc80c4da53f362a9bfe187b8fc647e4e0da26a28a86e7

Observation 356a7697-75f7-4b66-8575-c415faff3053 · outbound

This paper cites Yarrow Baldock, and Kimberly Gleason.

Representation learning with a transformer by contrastive learning for money laundering detection Yarrow Baldock, and Kimberly Gleason

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:50.950260Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:45.922975Z digest=sha256:381eab910b3cb23b5f5eae698a8b6b581328612aaa9fdc9e0cae2e28bc014644

Observation 5c04d291-d201-4dbe-be7f-6b50c3096e8f · outbound

This paper cites Long short-term memory.

Representation learning with a transformer by contrastive learning for money laundering detection Long short-term memory

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:50.844635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:46.005546Z digest=sha256:460c66239c541e23ea823660b2155c5c2bff8b344d4b4415ac08ad21a67840ea

Observation 9303511f-58b6-4c41-b23a-a10a5949a5a4 · outbound

This paper cites Modern information retrieval: A brief overview.

Representation learning with a transformer by contrastive learning for money laundering detection Modern information retrieval: A brief overview

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:50.627551Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:46.141958Z digest=sha256:0a10184dfcffa3876484f9d80c6f9936f8bde7bd32b8e35efbdc868a07f50394

Observation c7735681-a97a-4d35-9886-cf835fea850e · outbound

This paper cites Unsupervised learning of video representations using lstms, 2016.

Representation learning with a transformer by contrastive learning for money laundering detection Unsupervised learning of video representations using lstms, 2016

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:50.330216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:46.227024Z digest=sha256:96b3b3b71a3176deb6ebec63aede1225e497217c7cb39c34c48196d85a484714

Observation 08b8b426-385c-4dd2-aa6a-9acafeb51d02 · outbound

This paper cites Statistical methods for fighting financial crimes.

Representation learning with a transformer by contrastive learning for money laundering detection Statistical methods for fighting financial crimes

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:50.051227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:46.368477Z digest=sha256:6660fe868706751f05307b400acf1dc4652272feab4b58e2ff32c70e9a8fe0c6

Observation f3e4f4c1-0cad-4a77-8a1d-4fa124c554a3 · outbound

This paper cites A direct approach to false discovery rates.

Representation learning with a transformer by contrastive learning for money laundering detection A direct approach to false discovery rates

Reference 55

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:49.824139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:46.462351Z digest=sha256:cd3a4cdf1298187dedb6493123247b73dcc0cb3a790378db05bfa10a94669ed6

Observation d1045faa-2a52-4108-ae0b-88b7aa16d10a · outbound

This paper cites Hamlet: A transformer based approach for money laundering detection.

Representation learning with a transformer by contrastive learning for money laundering detection Hamlet: A transformer based approach for money laundering detection

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:49.720161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:46.543709Z digest=sha256:5930c562eb6660f86f8767c698cd11e39f9833e17c7ca70051ea8eb0aee7cd62

Observation c2f9b8be-81e6-43a9-8853-e297dce4cbd7 · outbound

This paper cites Scalable and imbalance-resistant machine learning models for anti-money laundering: A two-layered approach.

Representation learning with a transformer by contrastive learning for money laundering detection Scalable and imbalance-resistant machine learning models for anti-money laundering: A two-layered approach

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:49.523378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:46.680820Z digest=sha256:2c1f0067eb5e89cc6eac77fe4fa15ba69ab62da84a506a92f1bbcc0279fb02e4

Observation 0a8c8232-9555-4d3c-a046-60a0b9dad4f8 · outbound

This paper cites Representation learning with contrastive predictive coding, 2019.

Representation learning with a transformer by contrastive learning for money laundering detection Representation learning with contrastive predictive coding, 2019

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-06T19:48:46.801132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T19:48:46.801132Z digest=sha256:51b60d3091a8612d6239319cc5b1b257de153140c1803c07ec83730a8e037ea3

Observation 4db7aa4a-f8cc-45e3-a86c-ff7f611d42d3 · outbound

This paper cites van Houwelingen and S.

Representation learning with a transformer by contrastive learning for money laundering detection van Houwelingen and S

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:49.242087Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:46.928990Z digest=sha256:d18091d49207fed8c0aa2a343713c9ecf9ae9879882dc8d9cd481a705781a2c1

Observation 9e172550-347a-4c20-ba88-de4a3a3bf0e1 · outbound

This paper cites Gomez, Lukasz Kaiser, and Illia Polosukhin.

Representation learning with a transformer by contrastive learning for money laundering detection Gomez, Lukasz Kaiser, and Illia Polosukhin

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:49.012559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:47.069220Z digest=sha256:d8630cf551cc86c43ce54476ca23dc3aa248725c9595ff914bfe7e2c794db47c

Observation 5d215729-8cd6-41dc-854b-01ef8d420b2d · outbound

This paper cites What do position embeddings learn? an empirical study of pre-trained language model positional encoding, 2020.

Representation learning with a transformer by contrastive learning for money laundering detection What do position embeddings learn? an empirical study of pre-trained language model positional encoding, 2020

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:48.787764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:47.196002Z digest=sha256:0b9651eefcfe83ec1da5ce4f1a5502bccb4ab22e0cfe851b087d071e2f86a9d9

Observation 66dcea8d-6845-4c67-a37c-0e3163216899 · outbound

This paper cites Transformers in time series: A survey.

Representation learning with a transformer by contrastive learning for money laundering detection Transformers in time series: A survey

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:48.530141Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:47.328687Z digest=sha256:94753fd69a227a26ead0f1c24acd2475a73ea70684f4821b2a25f00bdf9350c0

Observation 7c52bdf9-c569-4db4-9682-023359ae45e5 · outbound

This paper cites Investigating the benefits of projection head for representation learning, 2024.

Representation learning with a transformer by contrastive learning for money laundering detection Investigating the benefits of projection head for representation learning, 2024

Reference 63

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:48.381769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:47.421196Z digest=sha256:6fa9b16dccd648c28933e35809945c60be2ccfd94a1105958c4fcd5c91378e68

Observation a2b45cbe-0fd6-49cc-adeb-10ffc9be9b81 · outbound

This paper cites Bronstein, and Or Litany.

Representation learning with a transformer by contrastive learning for money laundering detection Bronstein, and Or Litany

Reference 64

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:48.134922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:47.511988Z digest=sha256:efb0cc84af7ff8f79dbb64d0dff380bfad51f2f5ec0c743124b2c4e2cd6c595a

Observation 9cc6fb86-01f9-40c9-9092-f93bc1ae20e7 · outbound

This paper cites Comparative study on the performance of categorical variable encoders in classification and regression tasks, 2024.

Representation learning with a transformer by contrastive learning for money laundering detection Comparative study on the performance of categorical variable encoders in classification and regression tasks, 2024

Reference 65

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T19:48:47.914294Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=arxiv_source observed=2026-08-06T19:48:47.606453Z digest=sha256:cb450000f2a7627d7053a59819658f05b5a08135b06b6f9507119508cb344c04

Pith citing papers

No inbound Pith citation observations are available.