A Minimum Description Length Approach to Regularization in Neural Networks | Paper Citation Record

Observation 2e620648-d9ac-43b4-b6cb-4bfa614e4677 · outbound

This paper cites Minimum Description Length Hopfield Networks.

A Minimum Description Length Approach to Regularization in Neural Networks Minimum Description Length Hopfield Networks

Reference 1

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Observation 17c35ad9-b067-4500-a1dd-cc0f0c4d6f66 · outbound

This paper cites Review of parallel genetic algorithms bibliography.Technical Report, 1994.

A Minimum Description Length Approach to Regularization in Neural Networks Review of parallel genetic algorithms bibliography.Technical Report, 1994

Reference 2

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Observation 1fb2601d-a122-4814-a192-182e55dd7afb · outbound

This paper cites A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences.

A Minimum Description Length Approach to Regularization in Neural Networks A Systematic Analysis of Large Language Models as Soft Reasoners: The Case of Syllogistic Inferences

Reference 3

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Observation 66bb8432-dacf-4390-b937-b2ba84d274e7 · outbound

This paper cites PhD thesis, Massachusetts Inst.

A Minimum Description Length Approach to Regularization in Neural Networks PhD thesis, Massachusetts Inst

Reference 4

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Observation eeee77f0-e979-4843-9e2a-4caefaaff96b · outbound

This paper cites Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020.

A Minimum Description Length Approach to Regularization in Neural Networks Language models are few-shot learners.Advances in neural information processing systems, 33:1877–1901, 2020

Reference 5

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This paper cites A survey of parallel genetic algorithms.Calculateurs paralleles, reseaux et systems repartis, 10(2):141–171, 1998.

A Minimum Description Length Approach to Regularization in Neural Networks A survey of parallel genetic algorithms.Calculateurs paralleles, reseaux et systems repartis, 10(2):141–171, 1998

Reference 6

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Observation 51f03928-8069-435a-8e61-b5359814c7df · outbound

This paper cites Neural networks generalize on low complexity data.

A Minimum Description Length Approach to Regularization in Neural Networks Neural networks generalize on low complexity data

Reference 7

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Observation 917a66a3-7932-45e6-9d6d-a80a608cbeaa · outbound

This paper cites On the implicit bias of gradient descent for temporal extrapolation.

A Minimum Description Length Approach to Regularization in Neural Networks On the implicit bias of gradient descent for temporal extrapolation

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.444920Z

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Observation c99bde4f-fce8-4f3e-87a4-24dfc4b3d7cf · outbound

This paper cites Unsupervised Language Acquisition.

A Minimum Description Length Approach to Regularization in Neural Networks Unsupervised Language Acquisition

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:18:57.048292Z

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Observation 628474ad-5f68-4ac7-9501-44de0397886d · outbound

This paper cites Faith and fate: Limits of transformers on compositionality.Advances in Neural Information Processing Systems, 36: 70293–70332, 2023.

A Minimum Description Length Approach to Regularization in Neural Networks Faith and fate: Limits of transformers on compositionality.Advances in Neural Information Processing Systems, 36: 70293–70332, 2023

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.428060Z

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Observation 79d9c8f7-220b-4a3c-b877-b4b2fd5203ea · outbound

This paper cites Formal and empirical studies of counting behaviour in relu rnns.

A Minimum Description Length Approach to Regularization in Neural Networks Formal and empirical studies of counting behaviour in relu rnns

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.411738Z

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Observation b835650b-17f2-47e4-a2b2-69119fe602b5 · outbound

This paper cites Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks.

A Minimum Description Length Approach to Regularization in Neural Networks Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks

Reference 12

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Observation 19dec316-1cd7-4bfd-b88c-862e11eb6b35 · outbound

This paper cites Serial and parallel genetic algorithms as function optimiz- ers.

A Minimum Description Length Approach to Regularization in Neural Networks Serial and parallel genetic algorithms as function optimiz- ers

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.396777Z

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Observation 0840cffa-3c28-4c6a-bff2-3e40976f34c2 · outbound

This paper cites Inductive biases for deep learning of higher-level cognition.

A Minimum Description Length Approach to Regularization in Neural Networks Inductive biases for deep learning of higher-level cognition

Reference 14

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Observation 82a37551-282b-408a-b61b-d0e1eae62779 · outbound

This paper cites A minimum description length approach to grammar inference.

A Minimum Description Length Approach to Regularization in Neural Networks A minimum description length approach to grammar inference

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.368358Z

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Observation a6ae7bc1-3fa5-46d7-ba97-c7831929b5e2 · outbound

This paper cites Testing the limits of logical reasoning in neural and hybrid models.

A Minimum Description Length Approach to Regularization in Neural Networks Testing the limits of logical reasoning in neural and hybrid models

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.351157Z

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Observation 652e8021-d81f-4771-9696-3f926f25375a · outbound

This paper cites Keeping the neural networks simple by minimizing the description length of the weights.

A Minimum Description Length Approach to Regularization in Neural Networks Keeping the neural networks simple by minimizing the description length of the weights

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.333435Z

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Observation a138c918-aac5-4881-8126-c4b0d61466a6 · outbound

This paper cites MIT press, 1992.

A Minimum Description Length Approach to Regularization in Neural Networks MIT press, 1992

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.314856Z

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Observation ea22add9-d545-430f-a8c8-e9139f5561f8 · outbound

This paper cites Stanford University, 1969.

A Minimum Description Length Approach to Regularization in Neural Networks Stanford University, 1969

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.299593Z

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Observation d8ba7537-3cd8-4d8d-bb11-0832001dbbf8 · outbound

This paper cites InductionBench: LLMs Fail in the Simplest Complexity Class.

A Minimum Description Length Approach to Regularization in Neural Networks InductionBench: LLMs Fail in the Simplest Complexity Class

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:18:57.007165Z

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Observation 1ad0b5c0-157b-4a90-ab99-4b1319e99c58 · outbound

This paper cites macmillan, 2011.

A Minimum Description Length Approach to Regularization in Neural Networks macmillan, 2011

Reference 21

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Observation 3976e51e-59f7-4cb8-a4da-47382648356b · outbound

This paper cites Minimum description length recurrent neural networks.Transactions of the Association for Computational Linguistics, 10: 785–799, 2022.

A Minimum Description Length Approach to Regularization in Neural Networks Minimum description length recurrent neural networks.Transactions of the Association for Computational Linguistics, 10: 785–799, 2022

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.272137Z

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Observation adb73df5-b425-45ae-9de0-190b312b97ba · outbound

This paper cites Benchmarking neural network generalization for grammar induction.

A Minimum Description Length Approach to Regularization in Neural Networks Benchmarking neural network generalization for grammar induction

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.254257Z

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Observation 9748c59f-fde3-423b-b4e7-e575d28e8299 · outbound

This paper cites Bridging the Empirical-Theoretical Gap in Neural Network Formal Language Learning Using Minimum Description Length.

A Minimum Description Length Approach to Regularization in Neural Networks Bridging the Empirical-Theoretical Gap in Neural Network Formal Language Learning Using Minimum Description Length

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:18:56.982385Z

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Observation c9ebbe10-8e80-400c-8499-6b400a0033ea · outbound

This paper cites Springer, 2008.

A Minimum Description Length Approach to Regularization in Neural Networks Springer, 2008

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.235130Z

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Observation d192fd17-4d4c-4849-9e60-fbc9cfe7e5fa · outbound

This paper cites Deep neural networks have an inbuilt occam’s razor.Nature Communications, 16(1):220, 2025.

A Minimum Description Length Approach to Regularization in Neural Networks Deep neural networks have an inbuilt occam’s razor.Nature Communications, 16(1):220, 2025

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.217442Z

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Observation 19aecaa0-5eed-47b8-a0fd-0ec3b8602bfe · outbound

This paper cites GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.

A Minimum Description Length Approach to Regularization in Neural Networks GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:56.174117Z

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Observation d17023af-0617-4c99-9168-8866fc828a80 · outbound

This paper cites Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models.

A Minimum Description Length Approach to Regularization in Neural Networks Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:56.179472Z

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Observation 73f285d0-2879-41a8-8de7-632e18def59f · outbound

This paper cites Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics.

A Minimum Description Length Approach to Regularization in Neural Networks Arithmetic Without Algorithms: Language Models Solve Math With a Bag of Heuristics

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:56.183675Z

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Observation 93e4c020-b602-45e8-8291-796e69514d9b · outbound

This paper cites Bayesian learning of visual chunks by human observers.Proceedings of the National Academy of Sciences, 105(7):2745– 2750, 2008.

A Minimum Description Length Approach to Regularization in Neural Networks Bayesian learning of visual chunks by human observers.Proceedings of the National Academy of Sciences, 105(7):2745– 2750, 2008

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.199163Z

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Observation 86ea1c98-71d5-40a2-a99a-aaa2d86c35cf · outbound

This paper cites On evaluation metrics in optimality theory.Linguistic Inquiry, 47 (2):235–282, 2016.

A Minimum Description Length Approach to Regularization in Neural Networks On evaluation metrics in optimality theory.Linguistic Inquiry, 47 (2):235–282, 2016

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.181643Z

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Observation 1c4df4bf-c9f7-45be-9938-c3c94c1eb550 · outbound

This paper cites Approaching explanatory adequacy in phonology using minimum description length.Journal of Language Modelling, 9 (1):17–66, 2021.

A Minimum Description Length Approach to Regularization in Neural Networks Approaching explanatory adequacy in phonology using minimum description length.Journal of Language Modelling, 9 (1):17–66, 2021

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.163435Z

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Observation d01045cd-10d9-4e38-a094-84b218f9d0ff · outbound

This paper cites Modeling by shortest data description.Automatica, 14(5):465–471, 1978.

A Minimum Description Length Approach to Regularization in Neural Networks Modeling by shortest data description.Automatica, 14(5):465–471, 1978

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:56.315127Z

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Observation c47436b2-3680-4c0b-9550-37ef9d39e09c · outbound

This paper cites Discovering neural nets with low kolmogorov complexity and high generalization capability.Neural Networks, 10(5):857–873, 1997.

A Minimum Description Length Approach to Regularization in Neural Networks Discovering neural nets with low kolmogorov complexity and high generalization capability.Neural Networks, 10(5):857–873, 1997

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.114536Z

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Observation 09347089-2823-4286-813c-8cef2a3a3d38 · outbound

This paper cites PhD thesis, Massachusetts Institute of Technology, 2009.

A Minimum Description Length Approach to Regularization in Neural Networks PhD thesis, Massachusetts Institute of Technology, 2009

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:58.020371Z

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Observation add65634-7b00-4c22-8845-e8c05944cc42 · outbound

This paper cites A mathematical theory of communication.The Bell system technical journal, 27(3):379–423, 1948.

A Minimum Description Length Approach to Regularization in Neural Networks A mathematical theory of communication.The Bell system technical journal, 27(3):379–423, 1948

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:56.329468Z

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Observation 8858dd8b-61da-45c0-be0a-6aaae158e86b · outbound

This paper cites On the computational power of neural nets.

A Minimum Description Length Approach to Regularization in Neural Networks On the computational power of neural nets

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:57.992293Z

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Observation 24222ce9-745e-4b64-b0dc-720e8bf67664 · outbound

This paper cites A formal theory of inductive inference.

A Minimum Description Length Approach to Regularization in Neural Networks A formal theory of inductive inference

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:57.974602Z

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Observation a96e6933-3019-483c-b4dc-12e900255a3b · outbound

This paper cites A provably stable neural network turing machine with finite precision and time.Information Sciences, 658:120034, 2024.

A Minimum Description Length Approach to Regularization in Neural Networks A provably stable neural network turing machine with finite precision and time.Information Sciences, 658:120034, 2024

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:57.934343Z

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Observation 3e32a1c2-e19d-40d3-bed4-999876d2fe19 · outbound

This paper cites University of California, Berkeley, 1994.

A Minimum Description Length Approach to Regularization in Neural Networks University of California, Berkeley, 1994

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:57.901231Z

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Observation 16942d4f-e811-45e6-b136-7303ce5765b1 · outbound

This paper cites How to grow a mind: Statistics, structure, and abstraction.science, 331(6022):1279–1285, 2011.

A Minimum Description Length Approach to Regularization in Neural Networks How to grow a mind: Statistics, structure, and abstraction.science, 331(6022):1279–1285, 2011

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:56.550917Z

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Observation 46e88359-5a07-4176-97b1-6407867d80e3 · outbound

This paper cites Evaluating the world model implicit in a generative model.Advances in Neural Information Processing Systems, 37:26941–26975, 2025.

A Minimum Description Length Approach to Regularization in Neural Networks Evaluating the world model implicit in a generative model.Advances in Neural Information Processing Systems, 37:26941–26975, 2025

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:57.789261Z

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Observation 4dc7db69-8b65-4070-af06-bfd2e0fc03d3 · outbound

This paper cites Large language models still can’t plan (a benchmark for llms on planning and reasoning about change).

A Minimum Description Length Approach to Regularization in Neural Networks Large language models still can’t plan (a benchmark for llms on planning and reasoning about change)

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:56.563209Z

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Observation f8233762-5209-4fbb-8e02-2754ce17965e · outbound

This paper cites Thinking like transformers.

A Minimum Description Length Approach to Regularization in Neural Networks Thinking like transformers

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:56.568493Z

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Observation d567f80e-c003-4203-9a3d-8d67ab0d2c3a · outbound

This paper cites Reasoning or reciting? exploring the capabilities and limitations of language models through counterfactual tasks.

A Minimum Description Length Approach to Regularization in Neural Networks Reasoning or reciting? exploring the capabilities and limitations of language models through counterfactual tasks

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:57.638794Z

Source-reported events for the cited work

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Observation 8fc172f8-c328-4ec5-9c83-156fda8a98ea · outbound

This paper cites Word learning as bayesian inference.Psychological review, 114(2):245, 2007.

A Minimum Description Length Approach to Regularization in Neural Networks Word learning as bayesian inference.Psychological review, 114(2):245, 2007

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:57.566074Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 97ce164b-ef22-46e6-92d2-88ad115e1bee · outbound

This paper cites When can transformers count to n?arXiv preprint arXiv:2407.15160, 2024.

A Minimum Description Length Approach to Regularization in Neural Networks When can transformers count to n?arXiv preprint arXiv:2407.15160, 2024

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-15T20:18:56.668970Z

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Observation f3e2adfe-096a-40e2-a34d-f998817fb9db · outbound

This paper cites Balancing accuracy and parsimony in genetic programming.Evolutionary Computation, 3(1):17–38, 1995.

A Minimum Description Length Approach to Regularization in Neural Networks Balancing accuracy and parsimony in genetic programming.Evolutionary Computation, 3(1):17–38, 1995

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:57.466257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 1702002b-7bd4-4bb3-8c22-23cc2bc6e6ee · outbound

This paper cites Evolving optimal neural networks using genetic algorithms with occam’s razor.Complex systems, 7(3):199–220, 1993.

A Minimum Description Length Approach to Regularization in Neural Networks Evolving optimal neural networks using genetic algorithms with occam’s razor.Complex systems, 7(3):199–220, 1993

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:18:57.306590Z

Source-reported events for the cited work

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Observation f046198a-3b83-4b2d-b405-6d71900ded75 · outbound

This paper cites On the Paradox of Learning to Reason from Data.

A Minimum Description Length Approach to Regularization in Neural Networks On the Paradox of Learning to Reason from Data

Reference 50

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