Paper Citation Record · LEDGER

Learning, fast and slow: a two-fold algorithm for data-based model adaptation

As of 17 August 2026, this Paper Citation Record lists 55 of 55 outbound references and 1 inbound Pith citation observation for arXiv:2507.12187.

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

pith.paper-citation-record.v1
2507.12187 v1

Coverage vector

measured 55 of 55 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T16:56:13.255398Z

measured 56 of 56 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-17T04:55:36.263833Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-17T04:59:04.093241Z

Reference resolution

55 of 55 outbound references displayed

verified exact0
verified fuzzy46
unresolved9
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bfca5dff-3d2b-4826-b876-43f488c9f66d · outbound

This paper cites A survey of uncertainty in deep neural networks,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation A survey of uncertainty in deep neural networks,

Reference 1

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Observation 1dc0e85c-f58d-4f0b-83fe-6644a4a2d9c5 · outbound

This paper cites Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning

Reference 2

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Observation e1a15fee-b04a-4995-9961-01a9c621e679 · outbound

This paper cites Open set recognition through deep neural network uncertainty: Does out-of-distribution detection require generative classifiers?.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Open set recognition through deep neural network uncertainty: Does out-of-distribution detection require generative classifiers?

Reference 3

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Observation 829596ac-31c0-4200-870f-777a3887f8de · outbound

This paper cites An ensemble learning framework for anomaly detection in building energy consumption,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation An ensemble learning framework for anomaly detection in building energy consumption,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:14.152919Z

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Observation 0ff8b4c7-ae0d-4702-81ac-8b6cef9dfca9 · outbound

This paper cites A novel ensemble learning approach to support building energy use prediction,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation A novel ensemble learning approach to support building energy use prediction,

Reference 5

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Observation eda9319b-4764-42de-bb0b-ac7abaef8a18 · outbound

This paper cites Bayesian multi-task learning MPC for robotic mobile manipulation,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Bayesian multi-task learning MPC for robotic mobile manipulation,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:14.122345Z

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Observation 206f4c0e-160e-4fcb-8bec-56ad44042a55 · outbound

This paper cites Learning-based on-track system identification for scaled autonomous racing in under a minute,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Learning-based on-track system identification for scaled autonomous racing in under a minute,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:14.106569Z

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Observation 5db34142-b0be-4108-9f89-7bf7bdceedbc · outbound

This paper cites Continual lifelong learning with neural networks: A review,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Continual lifelong learning with neural networks: A review,

Reference 8

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Observation 3b064650-7c41-40c7-a935-ecac0d60db38 · outbound

This paper cites Zhang and Y.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Zhang and Y

Reference 9

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Observation 7db18025-b806-4f36-9125-2cba86a0f3a3 · outbound

This paper cites Three types of incremental learning,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Three types of incremental learning,

Reference 10

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Observation 3b5b0ad0-7356-4730-b073-d64ad9b97369 · outbound

This paper cites Explainable data-driven modeling via mixture of experts: Towards effective blending of gray and black-box models,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Explainable data-driven modeling via mixture of experts: Towards effective blending of gray and black-box models,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:14.054185Z

Source-reported events for the cited work

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Observation 3324e47c-1905-4177-bc58-b93b7f318d50 · outbound

This paper cites Theory on Mixture-of-Experts in Continual Learning.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Theory on Mixture-of-Experts in Continual Learning

Reference 12

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Observation fa28214e-b82f-4d46-8e00-48ee4851d57b · outbound

This paper cites A neural network nonlinear multimodel ensemble to improve precipitation forecasts over continental US,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation A neural network nonlinear multimodel ensemble to improve precipitation forecasts over continental US,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:14.038362Z

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Observation a7c97bbf-c890-448b-ab31-6592c9c9a99e · outbound

This paper cites Machine learning-based predictive control of nonlinear processes. Part I: theory,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Machine learning-based predictive control of nonlinear processes. Part I: theory,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:14.021417Z

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Observation f03e408f-8bcc-4b2e-8645-ea533e115a36 · outbound

This paper cites Coscl: Cooperation of small continual learners is stronger than a big one,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Coscl: Cooperation of small continual learners is stronger than a big one,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:14.000208Z

Source-reported events for the cited work

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Observation 020a5020-d857-4023-9c8d-b134ebc10fd5 · outbound

This paper cites Adaptive mixtures of local experts,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Adaptive mixtures of local experts,

Reference 16

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Source-reported events for the cited work

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Observation 844386dd-f64c-45f8-a8f8-ec4059aa8415 · outbound

This paper cites Hierarchical mixtures of experts and the EM algorithm,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Hierarchical mixtures of experts and the EM algorithm,

Reference 17

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Source-reported events for the cited work

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Observation 49ff12fd-ca10-41da-a9de-58243f930d2e · outbound

This paper cites Convergence results for the EM approach to mixtures of experts architectures,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Convergence results for the EM approach to mixtures of experts architectures,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.952067Z

Source-reported events for the cited work

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Observation 788de365-0d6c-4ba8-89d2-dc1d09089934 · outbound

This paper cites A survey of ensemble learning: Concepts, algorithms, applications, and prospects,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation A survey of ensemble learning: Concepts, algorithms, applications, and prospects,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.935245Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 220c519c-1684-4c84-ba90-1915420384f5 · outbound

This paper cites Online learning: A comprehensive survey,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Online learning: A comprehensive survey,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.920183Z

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Observation 9e349029-e9e0-4c48-accb-77b33e2611cb · outbound

This paper cites Robust adaptive MPC using control contraction metrics,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Robust adaptive MPC using control contraction metrics,

Reference 21

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Observation 1b685277-3aa3-4b9b-aa4d-682795f47fa1 · outbound

This paper cites Robust MPC with recursive model update,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Robust MPC with recursive model update,

Reference 22

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Observation 1d1c76b7-9d93-4887-83b7-ed032fa8514f · outbound

This paper cites Adaptive model predictive safety certification for learning-based control,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Adaptive model predictive safety certification for learning-based control,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.876426Z

Source-reported events for the cited work

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Observation 7cbc558e-380e-48d2-bb34-059ac821d9b1 · outbound

This paper cites ANN model adaptation algorithm based on extended Kalman filter applied to pH control using MPC,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation ANN model adaptation algorithm based on extended Kalman filter applied to pH control using MPC,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.856478Z

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Observation f1f0aab1-e967-49db-8bdb-9c28428fad08 · outbound

This paper cites Physics-informed online learning of gray-box models by moving horizon estimation,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Physics-informed online learning of gray-box models by moving horizon estimation,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.838780Z

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Observation 70d63ce5-a3c6-49ba-a034-d5ca48080fb6 · outbound

This paper cites Towards lifelong learning of recurrent neural networks for control design,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Towards lifelong learning of recurrent neural networks for control design,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.822934Z

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Observation 1a1ac247-56b5-4975-ab74-eea7ab14a2b1 · outbound

This paper cites Hands-on bayesian neural networks—a tutorial for deep learning users,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Hands-on bayesian neural networks—a tutorial for deep learning users,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.805721Z

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Observation 2a0a3b34-795d-4b01-8979-6a0444d6dc0a · outbound

This paper cites Meta-learning priors for efficient online bayesian regression,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Meta-learning priors for efficient online bayesian regression,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.790062Z

Source-reported events for the cited work

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Observation 8293e6cc-fb9f-4606-b89c-3fd1b9053ca0 · outbound

This paper cites Bayesian layers: A module for neural network uncertainty,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Bayesian layers: A module for neural network uncertainty,

Reference 29

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Observation ea3ebc19-22ea-4072-8f3c-1ed567923345 · outbound

This paper cites Gaussian processes for dynamics learning in model predictive control.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Gaussian processes for dynamics learning in model predictive control

Reference 30

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Source-reported events for the cited work

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Observation 8d6cd735-d5be-44a7-bc25-73620fa1f242 · outbound

This paper cites Online learning-based model predictive control with Gaussian process models and stability guarantees,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Online learning-based model predictive control with Gaussian process models and stability guarantees,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.752103Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8864db9f-414b-481c-92fa-012ea48678c9 · outbound

This paper cites Learning-based model predictive control: Toward safe learning in control,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Learning-based model predictive control: Toward safe learning in control,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.736800Z

Source-reported events for the cited work

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Observation 5b5094bd-7eb1-4ce9-b28a-9242875e30cf · outbound

This paper cites Cautious model predictive control using gaussian process regression,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Cautious model predictive control using gaussian process regression,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.719922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fa3c63db-e750-4627-9285-4675328ad3d7 · outbound

This paper cites Contextual tuning of model predictive control for autonomous racing,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Contextual tuning of model predictive control for autonomous racing,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.704462Z

Source-reported events for the cited work

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Observation 66f96689-b811-41c1-8d92-13f41cc4c67f · outbound

This paper cites Online learning of MPC for autonomous racing,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Online learning of MPC for autonomous racing,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.688170Z

Source-reported events for the cited work

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Observation 99f1281e-e4ab-4a28-bfcc-3f0dcc3f5387 · outbound

This paper cites Thinking, fast and slow,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Thinking, fast and slow,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.672615Z

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Observation 25e3de15-f6db-4111-84e2-c41a457db8eb · outbound

This paper cites Thinking, Fast and Slow by Daniel Kahneman,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Thinking, Fast and Slow by Daniel Kahneman,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.656213Z

Source-reported events for the cited work

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Observation a9c700ba-a3a9-4e93-be7b-425e026c7a55 · outbound

This paper cites an unresolved cited work.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Unresolved cited work

Reference 38

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Source-reported events for the cited work

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Observation d4ca504f-4a9a-4421-89bc-fd9ebc1fd364 · outbound

This paper cites Nonlinear optimization of district heating networks,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Nonlinear optimization of district heating networks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.623291Z

Source-reported events for the cited work

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Observation 0cea00ec-deeb-41f7-bf64-b9022b404392 · outbound

This paper cites Lifelong learning for monitoring and adaptation of data-based dynamical models: a statistical process control approach,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Lifelong learning for monitoring and adaptation of data-based dynamical models: a statistical process control approach,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.606757Z

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Observation 16596358-1afb-4097-91b2-ec82bb22dfe3 · outbound

This paper cites Machine thinking, fast and slow,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Machine thinking, fast and slow,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.591296Z

Source-reported events for the cited work

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Observation efad5be0-67b1-41ba-8f68-f1cc92ae822f · outbound

This paper cites Thinking fast and slow in AI,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Thinking fast and slow in AI,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.575020Z

Source-reported events for the cited work

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Observation 9e88dd30-aa6e-4250-b80d-955a41ca84a7 · outbound

This paper cites Thinking fast and slow with deep learning and tree search,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Thinking fast and slow with deep learning and tree search,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.556100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 48573323-e993-49fb-a8a2-4134ed513370 · outbound

This paper cites McShane-Vaughn, The Probability Handbook.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation McShane-Vaughn, The Probability Handbook

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.540395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 287edcb5-ad22-43c9-bd6e-9089efed4cf6 · outbound

This paper cites Model predictive control: Theory and practice—a survey,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Model predictive control: Theory and practice—a survey,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-06T16:56:13.202897Z

Source-reported events for the cited work

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Observation eee2faa5-51f6-4123-8f23-84d3bfe3e8ef · outbound

This paper cites On the certainty equivalence principle and the optimal control of partially observed dynamic games,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation On the certainty equivalence principle and the optimal control of partially observed dynamic games,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.513164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2da1437d-e708-4428-8348-6079bc82de02 · outbound

This paper cites On recurrent neural networks for learning-based control: recent results and ideas for future developments,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation On recurrent neural networks for learning-based control: recent results and ideas for future developments,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.496827Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 17a66d54-e34a-4cc5-82fa-0bbde230ac98 · outbound

This paper cites Learning stable gaussian process state space models,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Learning stable gaussian process state space models,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.482388Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5b3da1b3-9f11-4cfe-89bd-80e77ebb44d3 · outbound

This paper cites Using the Nystr ¨om method to speed up kernel machines,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Using the Nystr ¨om method to speed up kernel machines,

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.466587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 263b9f6f-92d1-45dc-b94f-3881afbe87d5 · outbound

This paper cites Sparse spectrum gaussian process regression,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Sparse spectrum gaussian process regression,

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.447617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7944c2e0-3923-4e9f-b5cd-059b5df44956 · outbound

This paper cites Multivariable feedback design: Concepts for a classical/modern synthesis,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Multivariable feedback design: Concepts for a classical/modern synthesis,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.431691Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f69f8bee-9f7f-4c54-a6f9-fa1993e5889e · outbound

This paper cites Heat roadmap europe 4: quantifying the impact of low-carbon heating and cooling roadmaps,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Heat roadmap europe 4: quantifying the impact of low-carbon heating and cooling roadmaps,

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.417050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation feb5d8b5-74f1-401c-ad6a-aa30c6d456cb · outbound

This paper cites Optimal management and data-based predictive control of district heating systems: The Novate Milanese experimental case-study,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Optimal management and data-based predictive control of district heating systems: The Novate Milanese experimental case-study,

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.400678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9ffb86cd-2a80-4e53-9de0-22289848d58c · outbound

This paper cites Development and experimental validation of an open-source model library for district heating network simulation,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Development and experimental validation of an open-source model library for district heating network simulation,

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.380907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8ec6eff5-9a9e-47e4-afb2-c574b09c4a4f · outbound

This paper cites Physics-informed neural network modeling and predictive control of district heating systems,.

Learning, fast and slow: a two-fold algorithm for data-based model adaptation Physics-informed neural network modeling and predictive control of district heating systems,

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T16:56:13.362533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

Observation 7ca38a24-8683-4925-8c55-905d05d68a7f · inbound

Model Predictive Control and Moving Horizon Estimation using Statistically Weighted Data-Based Ensemble Models cites this paper.

Model Predictive Control and Moving Horizon Estimation using Statistically Weighted Data-Based Ensemble Models Learning, fast and slow: a two-fold algorithm for data-based model adaptation

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-17T04:59:04.095795Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.