A statistical physics framework for optimal learning | Paper Citation Record

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This paper cites Botvinick and Jonathan D.

A statistical physics framework for optimal learning Botvinick and Jonathan D

Reference 1

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This paper cites The easy-to-hard effect in human (homo sapiens) and rat (rattus norvegicus) auditory identification.Journal of Comparative Psychology, 122(2):132, 2008.

A statistical physics framework for optimal learning The easy-to-hard effect in human (homo sapiens) and rat (rattus norvegicus) auditory identification.Journal of Comparative Psychology, 122(2):132, 2008

Reference 2

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This paper cites Springer Nature, 2019.

A statistical physics framework for optimal learning Springer Nature, 2019

Reference 3

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This paper cites Random search for hyper-parameter optimization.The journal of machine learning research, 13(1):281–305, 2012.

A statistical physics framework for optimal learning Random search for hyper-parameter optimization.The journal of machine learning research, 13(1):281–305, 2012

Reference 4

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This paper cites Practical bayesian optimization of machine learning algorithms.Advances in neural information processing systems, 25, 2012.

A statistical physics framework for optimal learning Practical bayesian optimization of machine learning algorithms.Advances in neural information processing systems, 25, 2012

Reference 5

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This paper cites Gradient-based hyperparameter opti- mization through reversible learning.

A statistical physics framework for optimal learning Gradient-based hyperparameter opti- mization through reversible learning

Reference 6

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Observation a3c4d8d9-d987-406e-8bc8-12d947cea1e2 · outbound

This paper cites Model-agnostic meta-learning for fast adaptation of deep networks.

A statistical physics framework for optimal learning Model-agnostic meta-learning for fast adaptation of deep networks

Reference 7

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This paper cites Engel and C.

A statistical physics framework for optimal learning Engel and C

Reference 8

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This paper cites Optimal errors and phase transitions in high-dimensional generalized linear models.Proceedings of the National Academy of Sciences, 116(12):5451–5460, 2019.

A statistical physics framework for optimal learning Optimal errors and phase transitions in high-dimensional generalized linear models.Proceedings of the National Academy of Sciences, 116(12):5451–5460, 2019

Reference 9

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This paper cites Learning curves of generic features maps for realistic datasets with a teacher-student model.Advances in Neural Information Processing Systems, 34:18137–18151, 2021.

A statistical physics framework for optimal learning Learning curves of generic features maps for realistic datasets with a teacher-student model.Advances in Neural Information Processing Systems, 34:18137–18151, 2021

Reference 10

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Observation 7267e221-9cf0-4dc3-b6dc-1b59a696f661 · outbound

This paper cites The role of regularization in classification of high-dimensional noisy Gaussian mixture.

A statistical physics framework for optimal learning The role of regularization in classification of high-dimensional noisy Gaussian mixture

Reference 11

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This paper cites Gener- alisation error in learning with random features and the hidden manifold model.

A statistical physics framework for optimal learning Gener- alisation error in learning with random features and the hidden manifold model

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:55.339313Z

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Observation b0a5135e-f6ce-42e8-97d1-1a22c5dabb02 · outbound

This paper cites Dy- namics of stochastic gradient descent for two-layer neural networks in the teacher-student setup.

A statistical physics framework for optimal learning Dy- namics of stochastic gradient descent for two-layer neural networks in the teacher-student setup

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:55.220479Z

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Observation 2b0c1db0-c7b6-4134-b087-6dea6f7834ca · outbound

This paper cites Dynamical mean-field theory for stochastic gradient descent in gaussian mixture classification.Advances in Neural Information Processing Systems, 33:9540–9550, 2020.

A statistical physics framework for optimal learning Dynamical mean-field theory for stochastic gradient descent in gaussian mixture classification.Advances in Neural Information Processing Systems, 33:9540–9550, 2020

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:55.082169Z

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Observation 2c7a530f-abdc-41cd-b929-7b1eb6f1353d · outbound

This paper cites Self-consistent dynamical field theory of kernel evolution in wide neural networks.Advances in Neural Information Processing Systems, 35:32240–32256, 2022.

A statistical physics framework for optimal learning Self-consistent dynamical field theory of kernel evolution in wide neural networks.Advances in Neural Information Processing Systems, 35:32240–32256, 2022

Reference 15

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Observation bb764f37-37f5-4788-b0d3-498ffb1eeda1 · outbound

This paper cites An analytical theory of curriculum learning in teacher-student networks.

A statistical physics framework for optimal learning An analytical theory of curriculum learning in teacher-student networks

Reference 16

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Observation f6ee6bca-79f5-4704-a1f5-eac4089e52a4 · outbound

This paper cites Why do animals need shaping? a theory of task composition and curriculum learning.

A statistical physics framework for optimal learning Why do animals need shaping? a theory of task composition and curriculum learning

Reference 17

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Observation 133a8815-a4bb-45cd-a5f0-909fe21117b0 · outbound

This paper cites Curriculum learning in humans and neural networks, Mar 2025.

A statistical physics framework for optimal learning Curriculum learning in humans and neural networks, Mar 2025

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:54.637764Z

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This paper cites High-dimensional learning of narrow neural networks.Journal of Statistical Mechanics: Theory and Experiment, 2025(2):023402, 2025.

A statistical physics framework for optimal learning High-dimensional learning of narrow neural networks.Journal of Statistical Mechanics: Theory and Experiment, 2025(2):023402, 2025

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:54.460619Z

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Observation 0873e939-db2b-4cff-8722-1ea390868025 · outbound

This paper cites Learning by on-line gradient descent.Journal of Physics A: Mathematical and general, 28(3):643, 1995.

A statistical physics framework for optimal learning Learning by on-line gradient descent.Journal of Physics A: Mathematical and general, 28(3):643, 1995

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Observation ee986a4b-e6e9-4135-ac0c-83db804774b5 · outbound

This paper cites Exact solution for on-line learning in multilayer neural networks.

A statistical physics framework for optimal learning Exact solution for on-line learning in multilayer neural networks

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Observation ddce2ffb-9f31-4ac6-9842-d09b1f2d0117 · outbound

This paper cites Analysis of on-line training with optimal learning rates.Physical Review E, 58(5):6379, 1998.

A statistical physics framework for optimal learning Analysis of on-line training with optimal learning rates.Physical Review E, 58(5):6379, 1998

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Observation 3ad444c6-b75d-4f91-a3f6-00d765b92091 · outbound

This paper cites Meta-Learning Strategies through Value Maximization in Neural Networks.

A statistical physics framework for optimal learning Meta-Learning Strategies through Value Maximization in Neural Networks

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Observation c9872b6a-b8ab-4ab2-845c-04d8e6ae3952 · outbound

This paper cites Courier Corporation, 2004.

A statistical physics framework for optimal learning Courier Corporation, 2004

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Observation 31a490cd-0281-4cc4-a5b0-f3c92416853a · outbound

This paper cites SIAM, 2010.

A statistical physics framework for optimal learning SIAM, 2010

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Observation f9c912ea-a254-485c-8d0f-aae36345d3a3 · outbound

This paper cites Practical recommendations for gradient-based training of deep architectures.

A statistical physics framework for optimal learning Practical recommendations for gradient-based training of deep architectures

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:53.788367Z

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Observation a5c71fdd-f0fc-4144-b5b3-3aa5f6a141d0 · outbound

This paper cites Why warmup the learning rate? underlying mecha- nisms and improvements.Advances in Neural Information Processing Systems, 37:111760–111801, 2024.

A statistical physics framework for optimal learning Why warmup the learning rate? underlying mecha- nisms and improvements.Advances in Neural Information Processing Systems, 37:111760–111801, 2024

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:53.532450Z

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Observation 30a7c17c-b881-4505-bb64-0e5b9baf9795 · outbound

This paper cites Sgdr: Stochastic gradient descent with warm restarts.

A statistical physics framework for optimal learning Sgdr: Stochastic gradient descent with warm restarts

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Observation 1f596d8d-8405-488a-ad0d-f0e0f9dbaa97 · outbound

This paper cites Online learning rate adaptation with hypergradient descent.

A statistical physics framework for optimal learning Online learning rate adaptation with hypergradient descent

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:53.334964Z

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Observation c3d0100f-2999-44ca-a94b-e7440979f63e · outbound

This paper cites Globally optimal parameters for on-line learning in multilayer neural networks.Physical review letters, 79(13):2578, 1997.

A statistical physics framework for optimal learning Globally optimal parameters for on-line learning in multilayer neural networks.Physical review letters, 79(13):2578, 1997

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Observation 087bf65e-18c5-4e4c-9e32-d696057851df · outbound

This paper cites Optimization of on-line principal component analysis.Journal of Physics A: Mathematical and General, 32(22):4061, 1999.

A statistical physics framework for optimal learning Optimization of on-line principal component analysis.Journal of Physics A: Mathematical and General, 32(22):4061, 1999

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Observation f25371e4-785c-4b18-b5ac-b47287052cdb · outbound

This paper cites Optimal learning rate schedules in high-dimensional non-convex optimization problems.

A statistical physics framework for optimal learning Optimal learning rate schedules in high-dimensional non-convex optimization problems

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Observation ec5a4f11-f90d-43bd-ba8c-42ac9317ecb0 · outbound

This paper cites Optimal protocols for contin- ual learning via statistical physics and control theory.

A statistical physics framework for optimal learning Optimal protocols for contin- ual learning via statistical physics and control theory

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Observation 3a18c318-9fcc-4246-8672-8a54379891f8 · outbound

This paper cites Don’t decay the learning rate, increase the batch size.

A statistical physics framework for optimal learning Don’t decay the learning rate, increase the batch size

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Observation 2aedaa07-b91c-413e-9a29-baa686cf8fa1 · outbound

This paper cites Continual learning in the teacher-student setup: Impact of task similarity.

A statistical physics framework for optimal learning Continual learning in the teacher-student setup: Impact of task similarity

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Observation c14331a8-7684-4f47-afef-fd2293af8acf · outbound

This paper cites How catas- trophic can catastrophic forgetting be in linear regression? InConference on Learning Theory, pages 4028–4079.

A statistical physics framework for optimal learning How catas- trophic can catastrophic forgetting be in linear regression? InConference on Learning Theory, pages 4028–4079

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Observation e6c319e7-50f3-4aa4-a436-b51d324a83ea · outbound

This paper cites Order parameters and phase transitions of continual learning in deep neural networks.

A statistical physics framework for optimal learning Order parameters and phase transitions of continual learning in deep neural networks

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Observation 11d5977d-4ec7-4293-9992-cb062df1f4ed · outbound

This paper cites Provable advantage of curriculum learn- ing on parity targets with mixed inputs.

A statistical physics framework for optimal learning Provable advantage of curriculum learn- ing on parity targets with mixed inputs

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Observation 2a5483a1-e3b9-4011-bb3f-db4f63e3d885 · outbound

This paper cites Restoring data balance via generative models of t-cell receptors for antigen-binding prediction.bioRxiv, pages 2024–07, 2024.

A statistical physics framework for optimal learning Restoring data balance via generative models of t-cell receptors for antigen-binding prediction.bioRxiv, pages 2024–07, 2024

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Observation 320d9c14-234c-4bd5-977a-eb5bcde07cc6 · outbound

This paper cites Bias-inducing geometries: exactly solvable data model with fairness implications.

A statistical physics framework for optimal learning Bias-inducing geometries: exactly solvable data model with fairness implications

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Observation e6d109c5-9762-4929-b86f-0294b1ec16af · outbound

This paper cites Bias in motion: Theoretical insights into the dynamics of bias in sgd training.

A statistical physics framework for optimal learning Bias in motion: Theoretical insights into the dynamics of bias in sgd training

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Observation dcd81077-e14c-4a6a-89dc-0e939e936305 · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958, 2014.

A statistical physics framework for optimal learning Dropout: a simple way to prevent neural networks from overfitting.The journal of machine learning research, 15(1):1929–1958, 2014

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Observation 538afb0f-065c-400e-a146-1bc58b0226f7 · outbound

This paper cites Curriculum dropout.

A statistical physics framework for optimal learning Curriculum dropout

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Observation 607a7d12-7da2-4552-9f2a-1c16f332b9f8 · outbound

This paper cites Dropout reduces under- fitting.

A statistical physics framework for optimal learning Dropout reduces under- fitting

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:51.060653Z

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Observation 51ceb9d1-f9ee-4d53-8e8a-b3cb791b24b2 · outbound

This paper cites Analytic theory of dropout regularization.Phys.

A statistical physics framework for optimal learning Analytic theory of dropout regularization.Phys

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Observation 6140ae34-ea7b-496d-92db-0a352f367168 · outbound

This paper cites Outrageously large neural networks: The sparsely-gated mixture-of-experts layer.

A statistical physics framework for optimal learning Outrageously large neural networks: The sparsely-gated mixture-of-experts layer

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Observation 5de31c2d-e066-4e91-b718-6ac9da08e044 · outbound

This paper cites Learning phrase representations using RNN encoder–decoder for statistical machine translation.

A statistical physics framework for optimal learning Learning phrase representations using RNN encoder–decoder for statistical machine translation

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Observation 17d4c5db-05c7-4a3b-a5f7-62d8ff77bdb1 · outbound

This paper cites Gated linear networks.

A statistical physics framework for optimal learning Gated linear networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:50.502074Z

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Observation 23613871-7513-4824-a0f1-2086228c8d96 · outbound

This paper cites Globally gated deep linear networks.Advances in Neural Information Processing Systems, 35:34789–34801, 2022.

A statistical physics framework for optimal learning Globally gated deep linear networks.Advances in Neural Information Processing Systems, 35:34789–34801, 2022

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:50.274973Z

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Observation 8316a1bd-b9c5-4840-9f6b-e319d5022bff · outbound

This paper cites The neural race reduction: Dynamics of abstraction in gated networks.

A statistical physics framework for optimal learning The neural race reduction: Dynamics of abstraction in gated networks

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:50.127031Z

Source-reported events for the cited work

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Observation c8888dbb-f764-4364-bfe2-a1df5aa6b25c · outbound

This paper cites Nonlinear classification of neural manifolds with contextual information.Physical Review E, 111(3):035302, 2025.

A statistical physics framework for optimal learning Nonlinear classification of neural manifolds with contextual information.Physical Review E, 111(3):035302, 2025

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Source-reported events for the cited work

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Observation 6fc760d4-40aa-445b-8776-cd3025c1ef6e · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

A statistical physics framework for optimal learning Attention is all you need.Advances in neural information processing systems, 30, 2017

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Observation a6ecd214-c1df-447a-9777-20e2154c23ee · outbound

This paper cites Efficient content-based sparse attention with routing transformers.Transactions of the Association for Computational Linguistics, 9:53–68, 2021.

A statistical physics framework for optimal learning Efficient content-based sparse attention with routing transformers.Transactions of the Association for Computational Linguistics, 9:53–68, 2021

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Observation 6dfaa829-cd7f-442b-92cd-a45c03418a9f · outbound

This paper cites Adaptive atten- tion span in transformers.

A statistical physics framework for optimal learning Adaptive atten- tion span in transformers

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:49.779690Z

Source-reported events for the cited work

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Observation dbe94e78-2aaa-424b-a747-179edcd0cdb8 · outbound

This paper cites Are sixteen heads really better than one?Advances in neural information processing systems, 32, 2019.

A statistical physics framework for optimal learning Are sixteen heads really better than one?Advances in neural information processing systems, 32, 2019

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Observation 1c6716b1-6ac6-4d12-9d27-3abf771821fa · outbound

This paper cites The effects of information order and learning mode on schema abstraction.Memory & cognition, 12(1):20–30, 1984.

A statistical physics framework for optimal learning The effects of information order and learning mode on schema abstraction.Memory & cognition, 12(1):20–30, 1984

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:49.615918Z

Source-reported events for the cited work

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Observation baa6e266-40be-47a4-85d0-b0e451067fcc · outbound

This paper cites When does fading enhance perceptual category learning? Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(4):1162, 2013.

A statistical physics framework for optimal learning When does fading enhance perceptual category learning? Journal of Experimental Psychology: Learning, Memory, and Cognition, 39(4):1162, 2013

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:49.362898Z

Source-reported events for the cited work

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Observation 4b2db901-1f1d-4894-bf0f-0e9857e09656 · outbound

This paper cites Curriculum learning.

A statistical physics framework for optimal learning Curriculum learning

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-06T18:39:41.339550Z

Source-reported events for the cited work

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Observation c1ba915b-b44d-4ab4-971e-d262f5fbcba4 · outbound

This paper cites A survey on curriculum learning.IEEE transactions on pattern analysis and machine intelligence, 44(9):4555–4576, 2021.

A statistical physics framework for optimal learning A survey on curriculum learning.IEEE transactions on pattern analysis and machine intelligence, 44(9):4555–4576, 2021

Reference 59

Resolution: unresolved; no resolver link, observed 2026-08-06T18:39:41.395215Z

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Observation 9498c7bb-2b60-4853-a3c3-ff384c817cc5 · outbound

This paper cites On the power of curriculum learning in training deep networks.

A statistical physics framework for optimal learning On the power of curriculum learning in training deep networks

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:49.159581Z

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Observation bf678ef4-9b42-46d8-9c67-cc00b275df85 · outbound

This paper cites When do curricula work? InInternational Conference on Learning Representations, 2021.

A statistical physics framework for optimal learning When do curricula work? InInternational Conference on Learning Representations, 2021

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:49.023007Z

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Observation 18e144f6-6b0b-47fe-81f5-d9722b119d36 · outbound

This paper cites Theory of curriculum learning, with convex loss functions.

A statistical physics framework for optimal learning Theory of curriculum learning, with convex loss functions

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:48.887455Z

Source-reported events for the cited work

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Observation 2a18e7ce-4785-4060-b52e-e7469709107b · outbound

This paper cites an unresolved cited work.

A statistical physics framework for optimal learning Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:39:48.745524Z

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Observation 294fba1d-ef4d-476f-88c3-937ad5604d3a · outbound

This paper cites Curriculum learning by optimizing learning dynam- ics.

A statistical physics framework for optimal learning Curriculum learning by optimizing learning dynam- ics

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:48.620820Z

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Observation 15407a7a-1f06-4e1d-804d-c1fb58140c43 · outbound

This paper cites Rennie, Vaibhava Goel, and Samuel Thomas.

A statistical physics framework for optimal learning Rennie, Vaibhava Goel, and Samuel Thomas

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:48.433448Z

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Observation 0e0b0eec-91bb-4204-b93b-00053f386750 · outbound

This paper cites Extracting and composing robust features with denoising autoencoders.

A statistical physics framework for optimal learning Extracting and composing robust features with denoising autoencoders

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:48.283751Z

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Observation 0edaf180-9986-44f7-b8c5-894d63ab8c11 · outbound

This paper cites Denoising diffusion probabilistic models.

A statistical physics framework for optimal learning Denoising diffusion probabilistic models

Reference 67

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Source-reported events for the cited work

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This paper cites Learning dynamics of linear denoising au- toencoders.

A statistical physics framework for optimal learning Learning dynamics of linear denoising au- toencoders

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:48.147690Z

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Observation 61128815-a93e-4f82-b686-8b7c7bd73b59 · outbound

This paper cites High-dimensional asymptotics of denoising autoencoders.Ad- vances in Neural Information Processing Systems, 36:11850–11890, 2023.

A statistical physics framework for optimal learning High-dimensional asymptotics of denoising autoencoders.Ad- vances in Neural Information Processing Systems, 36:11850–11890, 2023

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:47.991457Z

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Observation f4bf8ce0-a759-43c8-861f-4defec3a0c28 · outbound

This paper cites A solvable model of learning generative diffusion: theory and insights.Advances in Neural Information Processing Systems, 38:5253–5296, 2026.

A statistical physics framework for optimal learning A solvable model of learning generative diffusion: theory and insights.Advances in Neural Information Processing Systems, 38:5253–5296, 2026

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:47.820017Z

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Observation 200773d0-0722-41d5-ab0a-596c529269c1 · outbound

This paper cites Geras and Charles Sutton.

A statistical physics framework for optimal learning Geras and Charles Sutton

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:47.688160Z

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Observation c01921b0-d2c3-4c48-9496-f01b4fd9dacb · outbound

This paper cites Non-uniform timestep sampling: Towards faster diffusion model training.

A statistical physics framework for optimal learning Non-uniform timestep sampling: Towards faster diffusion model training

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:47.594273Z

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Observation ce968619-6731-4813-b25b-bcf84f996514 · outbound

This paper cites Marginalized denoising auto- encoders for nonlinear representations.

A statistical physics framework for optimal learning Marginalized denoising auto- encoders for nonlinear representations

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:47.440290Z

Source-reported events for the cited work

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Observation 2910953f-2e6c-45a2-89f5-a5361f93c10b · outbound

This paper cites Modeling the influence of data structure on learning in neural networks: The hidden manifold model.Physical Review X, 10(4):041044, 2020.

A statistical physics framework for optimal learning Modeling the influence of data structure on learning in neural networks: The hidden manifold model.Physical Review X, 10(4):041044, 2020

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:47.386593Z

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Observation aefd29e1-dc19-43bf-8fdd-62a6a03d64a1 · outbound

This paper cites Classification of heavy-tailed features in high dimensions: a superstatistical approach.

A statistical physics framework for optimal learning Classification of heavy-tailed features in high dimensions: a superstatistical approach

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:47.258091Z

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Observation 7b010d21-1895-4ea0-8d82-3cdccc35e717 · outbound

This paper cites Wakhloo, Tamara J.

A statistical physics framework for optimal learning Wakhloo, Tamara J

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:47.126682Z

Source-reported events for the cited work

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Observation 30545258-c068-4a1b-8c4b-1a8b0f234001 · outbound

This paper cites Hierarchical clustering and dimensional reduction for optimal control of large-scale agent-based models.

A statistical physics framework for optimal learning Hierarchical clustering and dimensional reduction for optimal control of large-scale agent-based models

Reference 77

Resolution: verified exact; local_arxiv, observed 2026-08-06T18:39:44.876307Z

Source-reported events for the cited work

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Observation 3e1593d8-677b-4137-b4c1-aade41f72657 · outbound

This paper cites Some mathematical problems arising in connection with the theory of optimal au- tomatic control systems.

A statistical physics framework for optimal learning Some mathematical problems arising in connection with the theory of optimal au- tomatic control systems

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:47.019611Z

Source-reported events for the cited work

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Observation adbb0dba-1874-44a2-86b3-9135c9391f32 · outbound

This paper cites Casadi: a software framework for nonlinear optimization and optimal control.Mathematical Programming Computation, 11:1–36, 2019.

A statistical physics framework for optimal learning Casadi: a software framework for nonlinear optimization and optimal control.Mathematical Programming Computation, 11:1–36, 2019

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Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:46.913846Z

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

Observation b199d466-69e9-4813-9fc9-b1c5b9e5cde3 · outbound

This paper cites training time.

A statistical physics framework for optimal learning training time

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:46.784038Z

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Observation af963b0e-fd76-4152-be97-1b3a6528f2f0 · outbound

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A statistical physics framework for optimal learning Unresolved cited work

Reference 81

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:39:46.681522Z

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Observation e44eb0cb-b26f-4db0-ba51-4d2df4eb1536 · outbound

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A statistical physics framework for optimal learning Unresolved cited work

Reference 82

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:39:46.548044Z

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A statistical physics framework for optimal learning Unresolved cited work

Reference 83

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:39:46.417450Z

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A statistical physics framework for optimal learning Unresolved cited work

Reference 84

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:39:46.294812Z

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A statistical physics framework for optimal learning Unresolved cited work

Reference 85

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:39:46.186570Z

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Observation 4c114b19-0231-43e0-b9f1-2ef556e3654f · outbound

This paper cites an unresolved cited work.

A statistical physics framework for optimal learning Unresolved cited work

Reference 86

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:39:46.043042Z

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Observation 1c6f30cc-62c7-4340-a09a-88c8ce9801d2 · outbound

This paper cites an unresolved cited work.

A statistical physics framework for optimal learning Unresolved cited work

Reference 87

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:39:45.894187Z

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Observation 8784a780-b126-4670-9859-70f4b9028ab2 · outbound

This paper cites an unresolved cited work.

A statistical physics framework for optimal learning Unresolved cited work

Reference 88

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:39:45.779867Z

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Observation ff6fc80c-f053-4ba9-a721-afb5321064e0 · outbound

This paper cites an unresolved cited work.

A statistical physics framework for optimal learning Unresolved cited work

Reference 89

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:39:45.664158Z

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Observation 0d7b4d1e-0331-4f24-9fcb-83f3ecd6f128 · outbound

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A statistical physics framework for optimal learning Unresolved cited work

Reference 90

Resolution: unresolved; raw_fallback, observed 2026-08-06T18:39:45.508078Z

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Observation 8b114ec7-8a9c-4d89-8fa3-4a01e983e5e8 · outbound

This paper cites We typically choose the damping parameterγ damp >0.9.

A statistical physics framework for optimal learning We typically choose the damping parameterγ damp >0.9

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:39:45.283472Z

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