Paper Citation Record · LEDGER

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization

As of 15 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2606.29951.

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

pith.paper-citation-record.v1
2606.29951 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T07:45:45.144782Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

19 of 19 outbound references displayed

verified exact0
verified fuzzy0
unresolved19
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 501d3088-4342-4eba-aa74-a009554501b2 · outbound

This paper cites Optuna: A next-generation hyperparameter optimization framework.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Optuna: A next-generation hyperparameter optimization framework

Reference 1

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b1cdfd0d-1a9f-4ce1-b370-a3fe2bab6581 · outbound

This paper cites Tabnet: Attentive interpretable tabular learning.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Tabnet: Attentive interpretable tabular learning

Reference 2

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 325b2a0a-f9b0-489a-b2e6-4faea4613125 · outbound

This paper cites Openml benchmark- ing suites.Advances in neural information processing systems, 34, 2021.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Openml benchmark- ing suites.Advances in neural information processing systems, 34, 2021

Reference 3

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05dce53b-55ae-4be3-b6af-1fbbdbed909d · outbound

This paper cites Smote: synthetic minority over-sampling technique.Journal of artificial intelligence research, 16:321–357, 2002.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Smote: synthetic minority over-sampling technique.Journal of artificial intelligence research, 16:321–357, 2002

Reference 4

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3179e029-3934-42f9-990e-1db4055b887d · outbound

This paper cites Electronic health records to facilitate clinical research.Clinical Research in Cardiology, 106(1):1–9, 2017.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Electronic health records to facilitate clinical research.Clinical Research in Cardiology, 106(1):1–9, 2017

Reference 5

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54c6e8c3-2dfe-4d46-b730-90acb3aa3f41 · outbound

This paper cites Statistical and machine learning models in credit scoring: A systematic literature survey.Applied Soft Computing, 91:106263, 2020.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Statistical and machine learning models in credit scoring: A systematic literature survey.Applied Soft Computing, 91:106263, 2020

Reference 6

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23d05d7d-e66a-4102-b6d1-eb62f9aa3c26 · outbound

This paper cites Revisiting deep learning models for tabular data.Advances in neural information processing systems, 34:18932–18943, 2021.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Revisiting deep learning models for tabular data.Advances in neural information processing systems, 34:18932–18943, 2021

Reference 7

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37b1441d-017f-4993-beb0-5cfe46d0a7ff · outbound

This paper cites Why do tree-based models still out- perform deep learning on typical tabular data?Advances in neural information processing systems, 35:507–520, 2022.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Why do tree-based models still out- perform deep learning on typical tabular data?Advances in neural information processing systems, 35:507–520, 2022

Reference 8

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad9999a0-3392-4874-b2fd-c03e3735ab97 · outbound

This paper cites Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond.Journal of Machine Learning Research, 24(34):1–11, 2023.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Quantus: An explainable ai toolkit for responsible evaluation of neural network explanations and beyond.Journal of Machine Learning Research, 24(34):1–11, 2023

Reference 9

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 45a8240c-9151-4566-9d23-9ee5c3633459 · outbound

This paper cites A benchmark for interpretability methods in deep neural networks.Advances in neural information processing systems, 32, 2019.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization A benchmark for interpretability methods in deep neural networks.Advances in neural information processing systems, 32, 2019

Reference 10

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74520ef5-3202-496c-ad57-fbec1227573c · outbound

This paper cites Accelerated data-driven materials science with the materials project.Nature Materials, 24(10):1522–1532, 2025.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Accelerated data-driven materials science with the materials project.Nature Materials, 24(10):1522–1532, 2025

Reference 11

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e698eb4-4c82-4c87-bb13-028c8596f386 · outbound

This paper cites Well-tuned simple nets excel on tabular datasets.Advances in neural information processing systems, 34:23928– 23941, 2021.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Well-tuned simple nets excel on tabular datasets.Advances in neural information processing systems, 34:23928– 23941, 2021

Reference 12

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 953ab1eb-b3df-435c-b71f-233570348a00 · outbound

This paper cites Interpretable mesomorphic networks for tabular data.Advances in Neural Information Processing Systems, 37:31759– 31787, 2024.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Interpretable mesomorphic networks for tabular data.Advances in Neural Information Processing Systems, 37:31759– 31787, 2024

Reference 13

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ceb0326-80ae-4729-8c79-2e789542ad17 · outbound

This paper cites Synthetic benchmarks for scientific research in explainable machine learning.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Synthetic benchmarks for scientific research in explainable machine learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6e53f684-f120-4fd9-883e-56ef0ed5c9cb · outbound

This paper cites A unified approach to interpreting model predictions.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization A unified approach to interpreting model predictions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 464f213f-542e-4cd0-a95a-74a92a6de8ea · outbound

This paper cites Neural oblivious decision ensembles for deep learning on tabular data.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Neural oblivious decision ensembles for deep learning on tabular data

Reference 16

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation da0c40bb-751c-4042-a76b-1531a416daa7 · outbound

This paper cites Catboost: unbiased boosting with categorical features.Advances in neural information processing systems, 31, 2018.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Catboost: unbiased boosting with categorical features.Advances in neural information processing systems, 31, 2018

Reference 17

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86cdf74b-6dc1-41db-b0f2-0b2e76e3e658 · outbound

This paper cites ”why should i trust you?” explain- ing the predictions of any classifier.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization ”why should i trust you?” explain- ing the predictions of any classifier

Reference 18

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e3eb7d0-8cfa-42f6-a760-0f37a1c78940 · outbound

This paper cites Isaksen, Jørgen K.

Improved Predictive Performance and Interpretability for Mesomorphic Neural Networks Using Local Fidelity Regularization Isaksen, Jørgen K

Reference 19

Resolution: unresolved; no resolver link, observed 2026-06-30T07:45:45.144782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.