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Paper Citation Record · LEDGER

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony

As of 20 August 2026, this Paper Citation Record lists 62 of 62 outbound references and 0 inbound Pith citation observations for arXiv:2506.03302.

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

pith.paper-citation-record.v1
2506.03302 v2

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measured 62 of 62 reference resolution

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Pith citing papers itemized under the disclosed page cap.

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Reference resolution

62 of 62 outbound references displayed

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External citation measurements

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Outbound references

Observation 8e55ec2e-027c-4482-b7cd-65b2eea4dcbf · outbound

This paper cites Machine learning and the physical sciences,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Machine learning and the physical sciences,

Reference 1

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This paper cites Explainable machine learning for scientific insights and discoveries,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Explainable machine learning for scientific insights and discoveries,

Reference 2

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This paper cites Artificial intelligence: A powerful paradigm for scientific research,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Artificial intelligence: A powerful paradigm for scientific research,

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Scientific discovery in the age of artificial intelligence,

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This paper cites Physics-informed machine learning,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Physics-informed machine learning,

Reference 5

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This paper cites Physics-informed machine learning: case studies for weather and climate modelling,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Physics-informed machine learning: case studies for weather and climate modelling,

Reference 6

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This paper cites Learning skillful medium-range global weather forecasting,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Learning skillful medium-range global weather forecasting,

Reference 7

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Observation 2beef697-6a0b-4b71-ba20-5a581c97966b · outbound

This paper cites Highly accurate protein structure prediction with al- phafold,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Highly accurate protein structure prediction with al- phafold,

Reference 8

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This paper cites Data-driven modeling and learning in science and engineering,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Data-driven modeling and learning in science and engineering,

Reference 9

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This paper cites Perspectives on the integration between first-principles and data-driven modeling,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Perspectives on the integration between first-principles and data-driven modeling,

Reference 10

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This paper cites It’s just not that simple: an empirical study of the accuracy-explainability trade-off in machine learning for public policy,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony It’s just not that simple: an empirical study of the accuracy-explainability trade-off in machine learning for public policy,

Reference 11

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony How explainability contributes to trust in ai,

Reference 12

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This paper cites Ai and science: what 1,600 researchers think,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Ai and science: what 1,600 researchers think,

Reference 13

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Can we open the black box of AI?,

Reference 14

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This paper cites KAN: Kolmogorov–Arnold net- works,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony KAN: Kolmogorov–Arnold net- works,

Reference 15

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony KAN 2.0: Kolmogorov-Arnold Networks Meet Science

Reference 16

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This paper cites From PINNs to PIKANs: Recent advances in physics-informed machine learning,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony From PINNs to PIKANs: Recent advances in physics-informed machine learning,

Reference 17

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony KAN-ODEs: Kolmogorov– Arnold network ordinary differential equations for learning dynamical systems and hidden physics,

Reference 18

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Data-driven model discovery with Kolmogorov–Arnold networks,

Reference 19

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Neural architecture search: A survey,

Reference 20

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Deeply- supervised nets,

Reference 21

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This paper cites BranchyNet: Fast inference via early exiting from deep neural networks,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony BranchyNet: Fast inference via early exiting from deep neural networks,

Reference 22

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Conditional deep learning for energy-efficient and enhanced pattern recognition,

Reference 23

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Why should we add early exits to neural networks?,

Reference 24

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Deep residual learning for image recognition,

Reference 25

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This paper cites Multilayer feedfor- ward networks are universal approximators,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Multilayer feedfor- ward networks are universal approximators,

Reference 26

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Unresolved cited work

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony On functions of three variables,

Reference 28

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony On the representations of continuous functions of many variables by superposition of continuous functions of one variable and addition,

Reference 29

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This paper cites Kolmogorov-Arnold Networks are Radial Basis Function Networks.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Kolmogorov-Arnold Networks are Radial Basis Function Networks

Reference 30

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Enhancing Graph Collaborative Filtering with FourierKAN Feature Transformation

Reference 31

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony SineKAN: Kolmogorov–Arnold networks using sinusoidal activation func- tions,

Reference 32

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This paper cites Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Chebyshev Polynomial-Based Kolmogorov-Arnold Networks: An Efficient Architecture for Nonlinear Function Approximation

Reference 33

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Wav-KAN: Wavelet Kolmogorov-Arnold Networks

Reference 34

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Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony On the limited memory BFGS method for large scale optimization,

Reference 35

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raw_fallback, observed 2026-08-07T11:11:04.359005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.930681Z digest=sha256:55e4dcc277c1dcbeb514b28dd3764382d2813c57d07ee7ed701530aa53d4c99a

Observation b5680a6b-8cd5-4b90-b31d-df6e9fcac213 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Adam: A Method for Stochastic Optimization

Reference 36

Resolution
unresolved
no resolver link, observed 2026-08-07T11:11:03.933945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:11:03.933945Z digest=sha256:a4121b7f16aacdca29412b19548ab3a0b4ef820563ff63300b3577643f03c0a1

Observation 68043b85-e019-43fe-a1fa-39e6bc658b78 · outbound

This paper cites Bert loses patience: Fast and robust inference with early exit,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Bert loses patience: Fast and robust inference with early exit,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.349754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.937192Z digest=sha256:fb3bc92f8061363b8270c54c34881cfa6f4f3863711ed99c253c77ebc887023d

Observation 385e5b8e-f6fe-4c1a-ae1f-12556b207e91 · outbound

This paper cites BERxiT: Early exiting for BERT with better fine-tuning and extension to regression,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony BERxiT: Early exiting for BERT with better fine-tuning and extension to regression,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.341403Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.940503Z digest=sha256:668038652d7464c791e1d7dd0dc3f61bdafee77dd6d372f658393002ee058d16

Observation 94d2fd23-3c22-4bfe-b48e-4b50da188693 · outbound

This paper cites Adaptive inference through early-exit networks: Design, challenges and directions,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Adaptive inference through early-exit networks: Design, challenges and directions,

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.333226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.943420Z digest=sha256:8ff9853b6a4f983d09120736c8af61c838736b0d49fa753c10b1c80f1368a0f0

Observation 1dd4fd41-df39-4e05-a2ff-7d7d164fadec · outbound

This paper cites Early-exit deep neural network - a comprehensive survey,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Early-exit deep neural network - a comprehensive survey,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.325314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.946073Z digest=sha256:4a14955d2ddd5c0a82aea8de7e3c0d4a247302632d138bd437068cdf7dc999a0

Observation 4cf6d31c-d0f2-443c-9a19-949d81af7070 · outbound

This paper cites Densely connected convolutional networks,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Densely connected convolutional networks,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.317923Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.949539Z digest=sha256:ca0b3dd5206d92e77817b7ab18398aaeacd4ed4eaaa6a6a92346beb87e31ce12

Observation 06d65c58-dab0-4f3b-9c41-07010dcd5352 · outbound

This paper cites AI Feynman: A physics- inspired method for symbolic regression,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony AI Feynman: A physics- inspired method for symbolic regression,

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.310432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.953311Z digest=sha256:3a1442c83f1d26d031e9c98885e6fdcb103c5e1355768495400a86078c8c3199

Observation aaeb8390-a965-4dbb-aeaa-7ff8fc9100b8 · outbound

This paper cites AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.291866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.956184Z digest=sha256:6df875ed41532be15f4f06c95e81bd487c58ae0448732eba90ab7e6975923274

Observation d75531c1-e74d-4025-8833-9dba91627539 · outbound

This paper cites Multiple-valued stationary state and its instabil- ity of the transmitted light by a ring cavity system,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Multiple-valued stationary state and its instabil- ity of the transmitted light by a ring cavity system,

Reference 44

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.281858Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.959091Z digest=sha256:ab11329d9e3da6a393754d5fcd482546c64e18b17ef7ee0c8bb231eeaed93ed3

Observation 1002d8dd-f733-4bb4-957c-e0ce8e067651 · outbound

This paper cites Global dynamical behavior of the optical field in a ring cavity,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Global dynamical behavior of the optical field in a ring cavity,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.270974Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.962251Z digest=sha256:2031b1204063e62fddaad4797b21d5d2c69bd47b71f7d62f41aa37c4c80b3975

Observation 5a5edae7-640d-4c32-910e-00a0878257c8 · outbound

This paper cites Nonlinear dynamics and population disappearances,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Nonlinear dynamics and population disappearances,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.260579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.965310Z digest=sha256:7610d377b8a49d9a676ab6a25b8d94c3d446039c46108d779d24763fde0f5018

Observation 97c78513-78a8-420a-aaee-d43056fb5b97 · outbound

This paper cites Distal Interference: Exploring the Limits of Model-Based Continual Learning.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Distal Interference: Exploring the Limits of Model-Based Continual Learning

Reference 47

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:11:04.089123Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.968149Z digest=sha256:ea03f1f71073e3601d6f2cbc375db918b9bcc827d972fa076e70b4d70a841eb0

Observation 3a146412-c1d8-450f-a830-c8b893fa13d7 · outbound

This paper cites Airfoil self-noise and prediction,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Airfoil self-noise and prediction,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.248578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.971258Z digest=sha256:53d5ecebf1739673a5163306bcd22a34990b8f7edceffefa7047433c2cd2d54f

Observation de75c24a-b017-42a4-98b6-d34321a68c68 · outbound

This paper cites Local and global learning methods for predicting power of a combined gas & steam turbine,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Local and global learning methods for predicting power of a combined gas & steam turbine,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.238091Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.974245Z digest=sha256:51c65cc90a5d2c9972c73633b13c4abef64226010b5286eb5dd5d5f77acb46f9

Observation 289d3b0c-8fea-4261-ae1b-8254fe77b1c7 · outbound

This paper cites Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Prediction of full load electrical power output of a base load operated combined cycle power plant using machine learning methods,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.228227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.978383Z digest=sha256:d317e866361ba84494e0e8915a176f101962ddbb729e33286d84019434757827

Observation 00f4535d-7a27-4029-a5ce-3d445dfff0d1 · outbound

This paper cites A data-driven statistical model for predicting the critical temperature of a superconductor,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony A data-driven statistical model for predicting the critical temperature of a superconductor,

Reference 51

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.218427Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.982525Z digest=sha256:1c6d14853ee65a09b6510222e9b5c56c32ce0bc5fcd7cd76c2d1919763a8efea

Observation 3a85051a-a598-4eb7-838c-b131538967d5 · outbound

This paper cites MDR SuperCon datasheet ver.240322.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony MDR SuperCon datasheet ver.240322

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.209685Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.986639Z digest=sha256:aa73cb537816e080f15ac45da94aad5790b317454485c18bd79484c6242a113f

Observation fbcf101c-116a-4ecb-84a2-ad4164faddfe · outbound

This paper cites The UCI machine learning repository,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony The UCI machine learning repository,

Reference 53

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.199821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.990390Z digest=sha256:cd133117fd3f1e861b5358e6146e9bf67674e3929c080f1d6ddd159d23f4e00e

Observation e20767f5-769a-4e5d-8b6b-6a4a6f52fcff · outbound

This paper cites Ensemble learning: A survey,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Ensemble learning: A survey,

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.188606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.993182Z digest=sha256:e6135c96ab8d81b865d73d01af0fdba516091036b0075b4856f04083a1d0f17c

Observation e26ed2e8-3947-45e2-94fd-071c86dd3933 · outbound

This paper cites an unresolved cited work.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Unresolved cited work

Reference 55

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:11:04.178603Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.996159Z digest=sha256:a5ef82ba30d563664e61e6c7c1d8af38a6b7582ef6a78f09ebed176816579c14

Observation 437801d0-adf3-438f-a590-bb4e26e9f10b · outbound

This paper cites A review of uncertainty quantification in deep learning: Techniques, applications and challenges,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony A review of uncertainty quantification in deep learning: Techniques, applications and challenges,

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.168395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.999130Z digest=sha256:d62188c705bfa014df5c074867ba3a0b535bf813a2db25775a0cb1e381fc40f5

Observation 7da88c25-96a6-4719-950d-576277de856a · outbound

This paper cites Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Bayesian Kolmogorov Arnold Networks (Bayesian_KANs): A Probabilistic Approach to Enhance Accuracy and Interpretability

Reference 57

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:11:04.074657Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:04.002076Z digest=sha256:50c5a92b930bc84eabdd0b8da07e90de269a0effe999ee946e0538ddaf46cfd6

Observation 8fe07d64-d00a-4e4e-93d1-bd0e265e4d30 · outbound

This paper cites Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Conformalized-KANs: Uncertainty Quantification with Coverage Guarantees for Kolmogorov-Arnold Networks (KANs) in Scientific Machine Learning

Reference 58

Resolution
verified exact
local_arxiv, observed 2026-08-07T11:11:04.060284Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:04.005789Z digest=sha256:55e79aabefea5e180ccc4ac2558aa21a77853fa05a1766dc7524a6cb093e3c0f

Observation 10818575-a76b-4cbf-b03d-185ec90eef80 · outbound

This paper cites DARTS: Differentiable architecture search,.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony DARTS: Differentiable architecture search,

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.157665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:04.009557Z digest=sha256:ff278526199d3a7e13cdce44fafd2da77c2fc6ea9371999be86c62b821ef16b5

Observation 1a8d1f55-150f-4610-a6d2-3ae8ba5af3d8 · outbound

This paper cites PyTorch: An Imperative Style, High-Performance Deep Learning Library.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony PyTorch: An Imperative Style, High-Performance Deep Learning Library

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T11:11:04.013104Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:11:04.013104Z digest=sha256:bc0754934d4a9736fb13a853411cea93c54b47791c384288ab48fee4bda0cd09

Observation a6bf706a-2ba8-4db8-913c-d608809319a9 · outbound

This paper cites FourierKAN.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony FourierKAN

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T11:11:04.147300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:04.016511Z digest=sha256:d268984c8663441457989485d2730200c895d0163705dc7f5d2acb81e4f6755c

Observation be778af0-1bb9-4976-b331-5b1bcfcba2f7 · outbound

This paper cites an unresolved cited work.

Multi-Exit Kolmogorov-Arnold Networks: enhancing accuracy and parsimony Unresolved cited work

Reference 2019

Resolution
unresolved
raw_fallback, observed 2026-08-07T11:11:04.551086Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-08-07T11:11:03.836890Z digest=sha256:b4f1843cb90f363e01a324bf9d58072a1ca7448b84c57486ef55c6db505da0d4

Pith citing papers

No inbound Pith citation observations are available.