Paper Citation Record · LEDGER

Improving the performance of weak supervision searches using data augmentation

As of 17 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2412.00198.

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

pith.paper-citation-record.v1
2412.00198 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T05:42:37.566048Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

37 of 37 outbound references displayed

verified exact3
verified fuzzy2
unresolved32
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5d83160d-d8eb-4c2e-ba2e-d9c5e5058b14 · outbound

This paper cites Topological Obstructions to Autoencoding.

Improving the performance of weak supervision searches using data augmentation Topological Obstructions to Autoencoding

Reference 1

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Observation 9c84ee4a-bfde-42e0-98af-e4a634362eab · outbound

This paper cites Searching for New Physics with Deep Autoencoders.

Improving the performance of weak supervision searches using data augmentation Searching for New Physics with Deep Autoencoders

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.445661Z

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Observation 705cb61b-1a56-49db-9871-0af904f78b7e · outbound

This paper cites Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector.

Improving the performance of weak supervision searches using data augmentation Dijet resonance search with weak supervision using $\sqrt{s}=13$ TeV $pp$ collisions in the ATLAS detector

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.449382Z

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Observation e40405e9-f1ea-4166-a301-8a505b45541a · outbound

This paper cites Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at √s= 13 TeV,.

Improving the performance of weak supervision searches using data augmentation Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at √s= 13 TeV,

Reference 4

Resolution: verified exact; raw_fallback, observed 2026-08-12T05:42:37.942390Z

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Observation ff97a93b-88b6-497c-9994-13c5201375e2 · outbound

This paper cites Classification without labels: Learning from mixed samples in high energy physics.

Improving the performance of weak supervision searches using data augmentation Classification without labels: Learning from mixed samples in high energy physics

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.456786Z

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Observation 0ce4167e-dd23-442b-8d45-d31c053d021a · outbound

This paper cites On the Problem of the Most Efficient Tests of Statistical Hypotheses,.

Improving the performance of weak supervision searches using data augmentation On the Problem of the Most Efficient Tests of Statistical Hypotheses,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:42:37.989456Z

Source-reported events for the cited work

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Observation 9c2922c1-976e-402a-b931-ed04403aefc1 · outbound

This paper cites Anomaly Detection for Resonant New Physics with Machine Learning.

Improving the performance of weak supervision searches using data augmentation Anomaly Detection for Resonant New Physics with Machine Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.463866Z

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Observation c9a7f0ba-f98f-41cf-9684-aec053db3ec0 · outbound

This paper cites Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection.

Improving the performance of weak supervision searches using data augmentation Comparing Weak- and Unsupervised Methods for Resonant Anomaly Detection

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.467593Z

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Observation 5c4b4969-3191-4c85-ab87-e3cc675cb06e · outbound

This paper cites Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection.

Improving the performance of weak supervision searches using data augmentation Back To The Roots: Tree-Based Algorithms for Weakly Supervised Anomaly Detection

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.471179Z

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Observation e8c38786-d147-4725-8105-4435d9eaf459 · outbound

This paper cites Anomaly Detection in Presence of Irrelevant Features.

Improving the performance of weak supervision searches using data augmentation Anomaly Detection in Presence of Irrelevant Features

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.475656Z

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Observation 975cfe5a-ffdd-4584-89d2-c9dac0f2ad81 · outbound

This paper cites Improving the performance of weak supervision searches using transfer and meta-learning.

Improving the performance of weak supervision searches using data augmentation Improving the performance of weak supervision searches using transfer and meta-learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.479191Z

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Observation d0266ade-58d3-46a4-901a-66085ce03822 · outbound

This paper cites Incorporating Physical Priors into Weakly-Supervised Anomaly Detection.

Improving the performance of weak supervision searches using data augmentation Incorporating Physical Priors into Weakly-Supervised Anomaly Detection

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.482542Z

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Observation 8d611c32-7105-4ad7-952e-528ecdbe61e0 · outbound

This paper cites Accelerating Resonance Searches via Signature-Oriented Pre-training.

Improving the performance of weak supervision searches using data augmentation Accelerating Resonance Searches via Signature-Oriented Pre-training

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.486122Z

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Observation 33e4c2be-0ec3-4c0b-97d3-400caddcb742 · outbound

This paper cites Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning.

Improving the performance of weak supervision searches using data augmentation Data Augmentation at the LHC through Analysis-specific Fast Simulation with Deep Learning

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-12T05:42:37.819723Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2e8087c0-d6de-4f2c-bb8c-bc9640edd922 · outbound

This paper cites Meta-learning and data augmentation for mass-generalised jet taggers.

Improving the performance of weak supervision searches using data augmentation Meta-learning and data augmentation for mass-generalised jet taggers

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.493530Z

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Observation a5b0ce7a-58e2-41c3-b775-4e97e9f3c3b4 · outbound

This paper cites Mass Agnostic Jet Taggers.

Improving the performance of weak supervision searches using data augmentation Mass Agnostic Jet Taggers

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.496866Z

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Observation 697a36fe-bf07-46ab-8501-d70107126fc7 · outbound

This paper cites Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation.

Improving the performance of weak supervision searches using data augmentation Extensive Studies of the Neutron Star Equation of State from the Deep Learning Inference with the Observational Data Augmentation

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.500011Z

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Observation 232b00cd-d1e5-4116-9555-36f3501b8251 · outbound

This paper cites Improving Photometric Redshift Estimates with Training Sample Augmentation.

Improving the performance of weak supervision searches using data augmentation Improving Photometric Redshift Estimates with Training Sample Augmentation

Reference 18

Resolution: verified exact; local_arxiv, observed 2026-08-12T05:42:37.783793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation a841f652-0342-4727-866a-ccdec708ee49 · outbound

This paper cites Discerning Secluded Sector gauge structures.

Improving the performance of weak supervision searches using data augmentation Discerning Secluded Sector gauge structures

Reference 19

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Observation 32b2db83-a368-410b-8367-bf7db21d4348 · outbound

This paper cites Visible Effects of Invisible Hidden Valley Radiation.

Improving the performance of weak supervision searches using data augmentation Visible Effects of Invisible Hidden Valley Radiation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.509747Z

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Observation c38e2180-c4b2-492a-9dde-52ccda896e92 · outbound

This paper cites Dark matter in Hidden Valley models with stable and unstable light dark mesons.

Improving the performance of weak supervision searches using data augmentation Dark matter in Hidden Valley models with stable and unstable light dark mesons

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.512764Z

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Observation d23365b8-d943-40e0-b73c-0172e8ff9a70 · outbound

This paper cites Theory, phenomenology, and experimental avenues for dark showers: a Snowmass 2021 report.

Improving the performance of weak supervision searches using data augmentation Theory, phenomenology, and experimental avenues for dark showers: a Snowmass 2021 report

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.516544Z

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Observation 0f191f01-71b0-4842-8105-e5d509801cdc · outbound

This paper cites An Introduction to PYTHIA 8.2.

Improving the performance of weak supervision searches using data augmentation An Introduction to PYTHIA 8.2

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.519944Z

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Observation 85ac2d69-dfb7-4abe-b255-02d8e01ee6cb · outbound

This paper cites Parton distributions with LHC data.

Improving the performance of weak supervision searches using data augmentation Parton distributions with LHC data

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.523075Z

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Observation 477de444-098e-4980-8710-71980524fff6 · outbound

This paper cites The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations.

Improving the performance of weak supervision searches using data augmentation The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.526412Z

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Observation e78dd20a-f0fc-4091-af98-a447e2889dea · outbound

This paper cites DELPHES 3, A modular framework for fast simulation of a generic collider experiment.

Improving the performance of weak supervision searches using data augmentation DELPHES 3, A modular framework for fast simulation of a generic collider experiment

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.529642Z

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Observation 8df4cf77-b2b0-44e1-bea7-b0a9b1e4178a · outbound

This paper cites FastJet user manual.

Improving the performance of weak supervision searches using data augmentation FastJet user manual

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.533620Z

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Observation f26c84f6-5b76-442f-8d76-a10794b93605 · outbound

This paper cites The anti-k_t jet clustering algorithm.

Improving the performance of weak supervision searches using data augmentation The anti-k_t jet clustering algorithm

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.536762Z

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Observation ac34b725-5e01-4cfb-b6d5-5639fe7ca6f1 · outbound

This paper cites The Machine Learning Landscape of Top Taggers.

Improving the performance of weak supervision searches using data augmentation The Machine Learning Landscape of Top Taggers

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.539909Z

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Observation 9099fd1e-06c7-49cb-a94e-e81145c626c5 · outbound

This paper cites Jet-Images -- Deep Learning Edition.

Improving the performance of weak supervision searches using data augmentation Jet-Images -- Deep Learning Edition

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.543059Z

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Observation 46706aac-0d90-4a8e-8fe6-52c838520ad2 · outbound

This paper cites Deep-learning Top Taggers or The End of QCD?.

Improving the performance of weak supervision searches using data augmentation Deep-learning Top Taggers or The End of QCD?

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.546286Z

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Observation 3cc509fd-1018-4f0d-8273-983b588193e3 · outbound

This paper cites Chollet et al., “Keras.” https://keras.io, 2015.

Improving the performance of weak supervision searches using data augmentation Chollet et al., “Keras.” https://keras.io, 2015

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.550446Z

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Observation e9145b89-0e8b-45ab-baeb-44355e2492a1 · outbound

This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems.

Improving the performance of weak supervision searches using data augmentation TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.553257Z

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Observation 47b4f2a6-4f0f-4f9f-b7e3-58e039d1b6ed · outbound

This paper cites Formulae for Estimating Significance,.

Improving the performance of weak supervision searches using data augmentation Formulae for Estimating Significance,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:42:37.974873Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation dba8f610-5cda-4b59-95bf-7191fe98b79c · outbound

This paper cites A comprehensive survey on data augmentation,.

Improving the performance of weak supervision searches using data augmentation A comprehensive survey on data augmentation,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.559728Z

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Observation 0d897df3-1306-4814-9870-c633f11edc64 · outbound

This paper cites Anomalies, Representations, and Self-Supervision.

Improving the performance of weak supervision searches using data augmentation Anomalies, Representations, and Self-Supervision

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.562687Z

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Observation 137f6058-6a52-45c9-9d57-f92795a2a9b4 · outbound

This paper cites Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at $\sqrt{s} =$ 13 TeV.

Improving the performance of weak supervision searches using data augmentation Search for high mass dijet resonances with a new background prediction method in proton-proton collisions at $\sqrt{s} =$ 13 TeV

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T05:42:37.566048Z

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Pith citing papers

No inbound Pith citation observations are available.