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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.

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measured 37 of 37 standing notices

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

37 of 37 outbound references displayed

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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

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

Reference 24

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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

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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

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

Reference 27

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

Reference 28

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

Reference 29

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

Reference 30

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

Reference 31

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

Reference 32

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

Reference 33

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

Reference 34

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

Reference 35

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

Reference 36

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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

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