Quantifying Correlations of Machine Learning Models | Paper Citation Record

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This paper cites Governance of artificial intelligence.

Quantifying Correlations of Machine Learning Models Governance of artificial intelligence

Reference 1

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This paper cites Artificial intelligence in radiation oncology.

Quantifying Correlations of Machine Learning Models Artificial intelligence in radiation oncology

Reference 2

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This paper cites Securing connected and autonomous vehicles: Challenges posed by adversarial machine learning and the way forward.

Quantifying Correlations of Machine Learning Models Securing connected and autonomous vehicles: Challenges posed by adversarial machine learning and the way forward

Reference 3

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This paper cites N Thakur, and Shakila Basheer.

Quantifying Correlations of Machine Learning Models N Thakur, and Shakila Basheer

Reference 4

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This paper cites Machine-learning- enabled cooperative perception for connected autonomous vehicles: Challenges and opportunities.

Quantifying Correlations of Machine Learning Models Machine-learning- enabled cooperative perception for connected autonomous vehicles: Challenges and opportunities

Reference 5

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This paper cites On the Opportunities and Risks of Foundation Models.

Quantifying Correlations of Machine Learning Models On the Opportunities and Risks of Foundation Models

Reference 6

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This paper cites The algorithmic leviathan: Arbitrariness, fairness, and opportunity in algorithmic decision-making systems.

Quantifying Correlations of Machine Learning Models The algorithmic leviathan: Arbitrariness, fairness, and opportunity in algorithmic decision-making systems

Reference 7

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Quantifying Correlations of Machine Learning Models Unresolved cited work

Reference 8

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This paper cites Kuncheva and Christopher J.

Quantifying Correlations of Machine Learning Models Kuncheva and Christopher J

Reference 9

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This paper cites The random subspace method for constructing decision forests.

Quantifying Correlations of Machine Learning Models The random subspace method for constructing decision forests

Reference 10

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Observation fa99f5cc-556f-4e3b-8bb6-b1d30d124970 · outbound

This paper cites A Unified Theory of Diversity in Ensemble Learning.

Quantifying Correlations of Machine Learning Models A Unified Theory of Diversity in Ensemble Learning

Reference 11

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This paper cites Bagging classifiers for fighting poisoning attacks in adversarial classification tasks.

Quantifying Correlations of Machine Learning Models Bagging classifiers for fighting poisoning attacks in adversarial classification tasks

Reference 12

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Observation 9d24de20-de09-42c4-be1b-85cc7b5abdeb · outbound

This paper cites Improving adversarial robustness via promoting ensemble diversity.

Quantifying Correlations of Machine Learning Models Improving adversarial robustness via promoting ensemble diversity

Reference 13

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Observation 7cc400fa-1b38-4118-a93f-7a1eadbd9dac · outbound

This paper cites Dibs: Diversity inducing information bottleneck in model ensembles.

Quantifying Correlations of Machine Learning Models Dibs: Diversity inducing information bottleneck in model ensembles

Reference 14

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This paper cites A Probabilistic Theory of Pattern Recognition.

Quantifying Correlations of Machine Learning Models A Probabilistic Theory of Pattern Recognition

Reference 15

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This paper cites Approximate statistical tests for comparing supervised classification learning algorithms.

Quantifying Correlations of Machine Learning Models Approximate statistical tests for comparing supervised classification learning algorithms

Reference 16

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This paper cites Inference for the generalization error.

Quantifying Correlations of Machine Learning Models Inference for the generalization error

Reference 17

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This paper cites No unbiased estimator of the variance of k-fold cross-validation.

Quantifying Correlations of Machine Learning Models No unbiased estimator of the variance of k-fold cross-validation

Reference 18

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This paper cites Bias in error estimation when using cross-validation for model selection.

Quantifying Correlations of Machine Learning Models Bias in error estimation when using cross-validation for model selection

Reference 19

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This paper cites Residual variance estimation in machine learning.

Quantifying Correlations of Machine Learning Models Residual variance estimation in machine learning

Reference 20

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This paper cites Analysis of variance of cross-validation estimators of the generalization error.

Quantifying Correlations of Machine Learning Models Analysis of variance of cross-validation estimators of the generalization error

Reference 21

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Quantifying Correlations of Machine Learning Models Unresolved cited work

Reference 22

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This paper cites Imagenet: A large-scale hierarchical image database.

Quantifying Correlations of Machine Learning Models Imagenet: A large-scale hierarchical image database

Reference 23

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This paper cites Lawrence Zitnick.

Quantifying Correlations of Machine Learning Models Lawrence Zitnick

Reference 24

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This paper cites Battery health prediction using fusion-based feature selection and machine learning.

Quantifying Correlations of Machine Learning Models Battery health prediction using fusion-based feature selection and machine learning

Reference 25

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Observation 420c8cf9-7f7a-4881-8aac-7225e509ec9a · outbound

This paper cites Analyzing electric vehicle battery health performance using supervised machine learning.

Quantifying Correlations of Machine Learning Models Analyzing electric vehicle battery health performance using supervised machine learning

Reference 26

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This paper cites Large language models: A deep dive, 2024.

Quantifying Correlations of Machine Learning Models Large language models: A deep dive, 2024

Reference 27

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This paper cites Pearson Correlation Coefficient, pages 1–4.

Quantifying Correlations of Machine Learning Models Pearson Correlation Coefficient, pages 1–4

Reference 28

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Quantifying Correlations of Machine Learning Models Unresolved cited work

Reference 29

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This paper cites Kelley Pace and Ronald Barry.

Quantifying Correlations of Machine Learning Models Kelley Pace and Ronald Barry

Reference 30

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

Quantifying Correlations of Machine Learning Models Learning multiple layers of features from tiny images

Reference 31

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This paper cites Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification.

Quantifying Correlations of Machine Learning Models Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification

Reference 32

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This paper cites Mnist handwritten digit database.

Quantifying Correlations of Machine Learning Models Mnist handwritten digit database

Reference 33

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

Quantifying Correlations of Machine Learning Models Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 34

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Quantifying Correlations of Machine Learning Models Unresolved cited work

Reference 35

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This paper cites https://huggingface.co/modelshttps: //huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment.

Quantifying Correlations of Machine Learning Models https://huggingface.co/modelshttps: //huggingface.co/datasets/zeroshot/twitter-financial-news-sentiment

Reference 36

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This paper cites CARER: Contextualized affect representations for emotion recognition.

Quantifying Correlations of Machine Learning Models CARER: Contextualized affect representations for emotion recognition

Reference 37

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This paper cites Character-level convolutional networks for text classification.

Quantifying Correlations of Machine Learning Models Character-level convolutional networks for text classification

Reference 38

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Quantifying Correlations of Machine Learning Models Scikit-learn: Machine learning in python

Reference 39

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This paper cites Tensorflow: A system for large-scale machine learning.

Quantifying Correlations of Machine Learning Models Tensorflow: A system for large-scale machine learning

Reference 40

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This paper cites https://huggingface.co/datasets.

Quantifying Correlations of Machine Learning Models https://huggingface.co/datasets

Reference 41

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This paper cites https://huggingface.co/models.

Quantifying Correlations of Machine Learning Models https://huggingface.co/models

Reference 42

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This paper cites Peft: State-of-the-art parameter- efficient fine-tuning methods.

Quantifying Correlations of Machine Learning Models Peft: State-of-the-art parameter- efficient fine-tuning methods

Reference 43

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This paper cites Statistical Models : Theory and Practice.

Quantifying Correlations of Machine Learning Models Statistical Models : Theory and Practice

Reference 44

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This paper cites The regression analysis of binary sequences.

Quantifying Correlations of Machine Learning Models The regression analysis of binary sequences

Reference 45

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Quantifying Correlations of Machine Learning Models Unresolved cited work

Reference 46

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This paper cites Xgboost: A scalable tree boosting system.

Quantifying Correlations of Machine Learning Models Xgboost: A scalable tree boosting system

Reference 47

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This paper cites Generalized Additive Models.

Quantifying Correlations of Machine Learning Models Generalized Additive Models

Reference 48

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This paper cites Rumelhart, Geoffrey E.

Quantifying Correlations of Machine Learning Models Rumelhart, Geoffrey E

Reference 49

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This paper cites Bengio, and Geoffrey Hinton.

Quantifying Correlations of Machine Learning Models Bengio, and Geoffrey Hinton

Reference 50

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This paper cites Deep residual learning for image recognition.

Quantifying Correlations of Machine Learning Models Deep residual learning for image recognition

Reference 51

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This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Quantifying Correlations of Machine Learning Models Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 52

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This paper cites Densely connected convolutional networks.

Quantifying Correlations of Machine Learning Models Densely connected convolutional networks

Reference 53

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This paper cites MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.

Quantifying Correlations of Machine Learning Models MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

Reference 54

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This paper cites Mobilenetv2: Inverted residuals and linear bottlenecks.

Quantifying Correlations of Machine Learning Models Mobilenetv2: Inverted residuals and linear bottlenecks

Reference 55

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This paper cites Mistral 7b, 2023.

Quantifying Correlations of Machine Learning Models Mistral 7b, 2023

Reference 56

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Observation 0327e54b-2198-411e-9ad3-a99db930ad54 · outbound

This paper cites The Llama 3 Herd of Models.

Quantifying Correlations of Machine Learning Models The Llama 3 Herd of Models

Reference 57

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This paper cites Qwen2 Technical Report.

Quantifying Correlations of Machine Learning Models Qwen2 Technical Report

Reference 58

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Observation 2b42262d-9c18-4ad0-b775-9a49a420a5ad · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Quantifying Correlations of Machine Learning Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 59

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Unavailable: canonical work link unavailable.

Observation 63042a1b-e6b9-41fb-abf9-c9f976427fa1 · outbound

This paper cites Aya 23: Open weight releases to further multilingual progress, 2024.

Quantifying Correlations of Machine Learning Models Aya 23: Open weight releases to further multilingual progress, 2024

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:13:33.089803Z

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Observation f97ab26c-92eb-4b48-a818-1fca885462f3 · outbound

This paper cites The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only.

Quantifying Correlations of Machine Learning Models The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-09T00:13:32.566158Z

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Unavailable: canonical work link unavailable.

Observation 577ce473-956e-4cb1-a132-6a1fde418348 · outbound

This paper cites Bloom-7b1, 2023.

Quantifying Correlations of Machine Learning Models Bloom-7b1, 2023

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:13:33.073877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d50a9aab-a231-40fe-9ccb-3a3c6582fd38 · outbound

This paper cites Phi-2: The surprising power of small language models.

Quantifying Correlations of Machine Learning Models Phi-2: The surprising power of small language models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-09T00:13:32.722955Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bde8bab9-3972-4319-b03f-3f5a86429d7e · outbound

This paper cites Correlation coefficients: Appropriate use and interpretation.

Quantifying Correlations of Machine Learning Models Correlation coefficients: Appropriate use and interpretation

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-09T00:13:33.046671Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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