Paper Citation Record · LEDGER

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization

As of 6 August 2026, this Paper Citation Record lists 51 of 51 outbound references and 1 inbound Pith citation observation for arXiv:2605.06599.

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

pith.paper-citation-record.v1
2605.06599 v1

Coverage vector

measured 51 of 51 reference resolution

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measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T10:18:18.606851Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

51 of 51 outbound references displayed

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External citation measurements

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Outbound references

Observation 60c90ff4-79be-4566-b6ba-f61b60394066 · outbound

This paper cites Learning without forgetting for vision-language models.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Learning without forgetting for vision-language models

Reference 1

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Observation e750d5b7-0dd2-474d-a9e2-6be8a94b9163 · outbound

This paper cites Graph foundation models: Concepts, opportunities and challenges.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Graph foundation models: Concepts, opportunities and challenges

Reference 2

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Observation dcbf6f37-6f6b-465d-963b-44f6a951451b · outbound

This paper cites Deformable graph transformer.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Deformable graph transformer

Reference 3

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Observation 0a7be8f3-9e30-45ff-92a5-fb0114820b6f · outbound

This paper cites Nas-ped: Neural architecture search for pedestrian detection.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Nas-ped: Neural architecture search for pedestrian detection

Reference 4

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Observation 787c5f82-d614-4afa-90ee-6867b6164851 · outbound

This paper cites Attention is all you need.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Attention is all you need

Reference 5

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Observation 388c4142-d70e-4a1d-b200-44c4e5ed3641 · outbound

This paper cites An image is worth 16x16 words: Transformers for image recognition at scale.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization An image is worth 16x16 words: Transformers for image recognition at scale

Reference 6

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Observation b9c13802-d82b-44e6-8883-270a481561ed · outbound

This paper cites Conformer: Convolution-augmented transformer for speech recognition.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Conformer: Convolution-augmented transformer for speech recognition

Reference 7

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Observation 21b8a29c-72fa-4614-972f-ca20f4f75489 · outbound

This paper cites Ambrosio, L.A.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Ambrosio, L.A

Reference 8

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Observation af84252f-1111-4202-b193-39badc660ddb · outbound

This paper cites A simple weight decay can improve generalization.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization A simple weight decay can improve generalization

Reference 9

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Observation b2f7a34d-c918-4322-9c1d-b6fa5d6c7b1a · outbound

This paper cites Decoupled weight decay regularization.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Decoupled weight decay regularization

Reference 10

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Observation e4cd867b-780d-4cec-8f57-72511d67ff9e · outbound

This paper cites Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Propagation of Chaos for Mean-Field Langevin Dynamics and its Application to Model Ensemble

Reference 11

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This paper cites Mean-field langevin dynamics: Exponential convergence and annealing.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Mean-field langevin dynamics: Exponential convergence and annealing

Reference 12

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Observation 69de145b-afa6-4aa5-bd20-5352cc900ea0 · outbound

This paper cites Bayesian learning via stochastic gradient langevin dynamics.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Bayesian learning via stochastic gradient langevin dynamics

Reference 13

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Observation d922ecf3-07c3-4e2f-b438-b64263c5126d · outbound

This paper cites Adding Gradient Noise Improves Learning for Very Deep Networks.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Adding Gradient Noise Improves Learning for Very Deep Networks

Reference 14

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Observation ce908e78-5b67-4b24-b330-0e580ed0d6e3 · outbound

This paper cites Stochastic gradient langevin dy- namics with variance reduction.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Stochastic gradient langevin dy- namics with variance reduction

Reference 15

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Observation 8e5bb3fe-bd2b-42cf-adf2-0b8582c824dd · outbound

This paper cites Weight decay induces low-rank attention layers.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Weight decay induces low-rank attention layers

Reference 16

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Observation 67ffa9c8-e3e1-4764-8a2d-b5c3a4084cc3 · outbound

This paper cites How does attention work in vision transformers? a visual analytics attempt.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization How does attention work in vision transformers? a visual analytics attempt

Reference 17

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Observation 0354a5e5-58cc-4f35-a109-849f3b046be7 · outbound

This paper cites The shaped transformer: Attention models in the infinite depth-and- width limit.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization The shaped transformer: Attention models in the infinite depth-and- width limit

Reference 18

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Observation 39f43703-60f2-4020-8300-ceb21532f679 · outbound

This paper cites Examining the shape of attention in transformers.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Examining the shape of attention in transformers

Reference 19

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This paper cites Mean-field theory of two- layers neural networks: dimension-free bounds and kernel limit.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Mean-field theory of two- layers neural networks: dimension-free bounds and kernel limit

Reference 20

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Observation c86b3c8d-e3c8-4c38-93df-5c1679f64140 · outbound

This paper cites (Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization (Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics

Reference 21

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Observation cf743467-d644-46e4-8a52-f345257d7ca3 · outbound

This paper cites Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Non-convex learning via stochastic gradient langevin dynamics: a nonasymptotic analysis

Reference 22

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Observation edd0622e-4ac4-4dc7-8714-1b508e6acce4 · outbound

This paper cites Logarithmic sobolev inequalities.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Logarithmic sobolev inequalities

Reference 23

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Observation ce3cb44f-32df-4fd7-86f1-f002b4bea656 · outbound

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Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Logarithmic sobolev inequalities essentials

Reference 24

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Observation 12987acc-bbdf-43bd-9b01-85e1c8e79e07 · outbound

This paper cites Diffusions hypercontractives.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Diffusions hypercontractives

Reference 25

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Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Lectures on logarithmic sobolev inequalities

Reference 26

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Observation 4b1e6a41-f4d5-4e82-810e-53722e857615 · outbound

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Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Mean-field theory of two-layers neural networks: dimension-free bounds and kernel limit

Reference 27

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Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Mean field analysis of neural networks: A central limit theorem

Reference 28

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This paper cites Global convergence of langevin dynamics based algorithms for nonconvex optimization.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Global convergence of langevin dynamics based algorithms for nonconvex optimization

Reference 29

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Observation 791972fb-88f5-4be1-b6b5-5efe46bbb043 · outbound

This paper cites Villani,Optimal Transport: Old and New, Grundlehren der mathematischen Wissenschaften.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Villani,Optimal Transport: Old and New, Grundlehren der mathematischen Wissenschaften

Reference 30

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This paper cites Improving learning to optimize using parameter symmetries.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Improving learning to optimize using parameter symmetries

Reference 31

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This paper cites How good is the bayes posterior in deep neural networks really?.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization How good is the bayes posterior in deep neural networks really?

Reference 32

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Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Cover and J.A

Reference 33

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This paper cites Catoni,PAC-Bayesian Supervised Classification: The Thermodynam- ics of Statistical Learning, vol.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Catoni,PAC-Bayesian Supervised Classification: The Thermodynam- ics of Statistical Learning, vol

Reference 34

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This paper cites Noise-regularised in- struction tuning improves llm robustness.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Noise-regularised in- struction tuning improves llm robustness

Reference 35

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This paper cites Ridge-less regres- sion, implicit regularisation, and generalisation.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Ridge-less regres- sion, implicit regularisation, and generalisation

Reference 36

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Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Backpack 1.2.0 documenta- tion: Hutchinson trace estimation

Reference 37

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This paper cites Penn treebank dataset.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Penn treebank dataset

Reference 38

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This paper cites Treebank-3 (ldc99t42).

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Treebank-3 (ldc99t42)

Reference 39

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This paper cites Wikitext-103 dataset.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Wikitext-103 dataset

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:27:49.434411Z

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This paper cites Salesforce/wikitext.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Salesforce/wikitext

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:27:49.402339Z

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Observation beb1a662-f164-4738-910e-7ec809268a42 · outbound

This paper cites DIV A: Deep un- folded network from quantum interactive patches for image restoration.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization DIV A: Deep un- folded network from quantum interactive patches for image restoration

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:27:49.399517Z

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Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Quantum algorithm for signal denoising

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:27:49.420758Z

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Observation 806825b1-77d6-4bc7-ae35-4cb97dae2eec · outbound

This paper cites Quantum mechanics-based signal and image representation: Application to de- noising.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Quantum mechanics-based signal and image representation: Application to de- noising

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:27:49.387915Z

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Observation 492617b9-3122-44a6-9dbf-9d2309482117 · outbound

This paper cites A quantum denoising-based resolution enhancement framework for 250-mhz and 500-mhz quantitative acoustic microscopy.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization A quantum denoising-based resolution enhancement framework for 250-mhz and 500-mhz quantitative acoustic microscopy

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:27:49.368538Z

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Observation 1a728a2e-ee1f-4dbf-9209-6cb1487168ac · outbound

This paper cites Enhancing 3d radio-frequency data in quantitative acoustic microscopy using quantum-driven prior at 250-mhz and 500-mhz.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Enhancing 3d radio-frequency data in quantitative acoustic microscopy using quantum-driven prior at 250-mhz and 500-mhz

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:27:49.365818Z

Source-reported events for the cited work

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Observation ff3e7399-e2e6-43c1-8147-ba58e711c188 · outbound

This paper cites Unsupervised physics-inspired deep learning network with application to dental computed tomography image restoration.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Unsupervised physics-inspired deep learning network with application to dental computed tomography image restoration

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:27:49.371491Z

Source-reported events for the cited work

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Observation b903d0ef-39fe-4430-8b16-58f716b2c303 · outbound

This paper cites Auto- matic tuning of denoising algorithms parameters without ground truth.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Auto- matic tuning of denoising algorithms parameters without ground truth

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:27:49.391040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation c2de7aa6-d092-4d13-b514-5a04ffc0b57e · outbound

This paper cites Chatgpt.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Chatgpt

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:27:49.403036Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation 37f2a141-c835-472e-bc73-218954667e4c · outbound

This paper cites Copilot.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Copilot

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:27:49.417589Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Observation ad19638a-3c24-41d1-872e-b339ee5a7686 · outbound

This paper cites Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix.

Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization Randomized algorithms for estimating the trace of an implicit symmetric positive semi-definite matrix

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-26T13:27:49.286751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

Pith citing papers

Observation 265d89ea-e46c-4a6c-875c-f22e97fc6d30 · inbound

The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability cites this paper.

The Blessing of Dimensionality: How Near-Orthogonality in High-Dimensional Spaces Explains Temporal Portability Weight-Decay Turns Transformer Loss Landscapes Villani: Functional-Analytic Foundations for Optimization and Generalization

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-01T10:18:18.606851Z

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