Paper Citation Record · LEDGER

Improving Adaptive Moment Optimization via Preconditioner Diagonalization

As of 9 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2502.07488.

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

pith.paper-citation-record.v1
2502.07488 v1

Coverage vector

measured 45 of 45 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T12:40:42.772228Z

measured 46 of 46 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T15:16:58.221358Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T10:51:21.208174Z

Reference resolution

45 of 45 outbound references displayed

verified exact0
verified fuzzy14
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9ff3673c-00bf-43f4-9a38-8e1ba1a324fa · outbound

This paper cites Fisher information and natural gradient learning in random deep networks.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Fisher information and natural gradient learning in random deep networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:40:43.204735Z

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Observation a1faf0f7-6720-4769-9149-368f6fb422e5 · outbound

This paper cites Scalable Second Order Optimization for Deep Learning.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Scalable Second Order Optimization for Deep Learning

Reference 2

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Observation 0b42b222-d8a5-4361-90a6-d3e3bbef40fb · outbound

This paper cites Numerical optimization: theoretical and practical aspects.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Numerical optimization: theoretical and practical aspects

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:40:43.193082Z

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Observation 19311ef6-1f96-40d6-8143-6e36827dd755 · outbound

This paper cites Practical gauss-newton optimisation for deep learning.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Practical gauss-newton optimisation for deep learning

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:40:43.181798Z

Source-reported events for the cited work

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Observation d80d7340-773c-4fc8-947f-5f75f5656915 · outbound

This paper cites Lion Secretly Solves Constrained Optimization: As Lyapunov Predicts.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Lion Secretly Solves Constrained Optimization: As Lyapunov Predicts

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T12:40:42.189563Z

Source-reported events for the cited work

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Observation 6574f7c3-4993-4272-a8ee-6f89730b2b15 · outbound

This paper cites Symbolic discovery of optimization algorithms.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Symbolic discovery of optimization algorithms

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:40:43.171270Z

Source-reported events for the cited work

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Observation 4ceb3334-28c6-41de-98e4-2de2786b8804 · outbound

This paper cites an unresolved cited work.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-08T12:40:43.160693Z

Source-reported events for the cited work

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Observation 9c457a4a-7eaa-41c9-983f-a551320b8fd9 · outbound

This paper cites Adaptive subgradient methods for online learning and stochastic optimization.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Adaptive subgradient methods for online learning and stochastic optimization

Reference 8

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Source-reported events for the cited work

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Observation 4d74f84b-e6f9-47f4-a236-c160e529e279 · outbound

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

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Model-agnostic meta-learning for fast adaptation of deep networks

Reference 9

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Source-reported events for the cited work

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Observation 739f93e1-af6b-446d-b3c2-9cd88cdc9b72 · outbound

This paper cites Practical methods of optimization.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Practical methods of optimization

Reference 10

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Source-reported events for the cited work

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Observation db6573a3-3b95-4023-89ee-e5a2e404548b · outbound

This paper cites Global convergence of stochastic gradient hamiltonian monte carlo for nonconvex stochastic optimization: Nonasymptotic performance bounds and momentum-based acceleration.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Global convergence of stochastic gradient hamiltonian monte carlo for nonconvex stochastic optimization: Nonasymptotic performance bounds and momentum-based acceleration

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:40:43.134162Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ee8bbd5f-4502-4309-8150-a4e8a838e64e · outbound

This paper cites Fast approximate natural gradient descent in a kronecker factored eigenbasis.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Fast approximate natural gradient descent in a kronecker factored eigenbasis

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:40:43.122673Z

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Observation d6aa1a4c-2a00-4a35-947a-52939f3a1752 · outbound

This paper cites Low-rank gradient approximation for memory-efficient on-device training of deep neural network.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Low-rank gradient approximation for memory-efficient on-device training of deep neural network

Reference 13

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Observation f1c07a84-fd80-4865-88ff-db80c54f2a29 · outbound

This paper cites A kronecker-factored approximate fisher matrix for convolution layers.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization A kronecker-factored approximate fisher matrix for convolution layers

Reference 14

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Observation 4362436c-8008-4cb4-a035-3faa035aafaa · outbound

This paper cites Shampoo: Preconditioned stochastic tensor optimization.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Shampoo: Preconditioned stochastic tensor optimization

Reference 15

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Observation efc383d0-48fd-45d1-9082-178b65955821 · outbound

This paper cites Gradient Descent Happens in a Tiny Subspace.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Gradient Descent Happens in a Tiny Subspace

Reference 16

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Observation cbd4949f-d459-4d3f-b64a-27ad3c22c975 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Adam: A Method for Stochastic Optimization

Reference 17

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Observation 4a56218b-9d7a-4914-8271-0d1a1ef6e4bc · outbound

This paper cites Federated Optimization: Distributed Machine Learning for On-Device Intelligence.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Reference 18

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Observation 4ee2ef16-3de8-498f-9667-616dc150ad73 · outbound

This paper cites Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Heavy-Tailed Class Imbalance and Why Adam Outperforms Gradient Descent on Language Models

Reference 19

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Observation d301ac9e-e0c3-4471-8e02-27667e5696b4 · outbound

This paper cites Federated learning: Challenges, methods, and future directions.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Federated learning: Challenges, methods, and future directions

Reference 20

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Source-reported events for the cited work

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Observation 0e4a53f3-10b6-493e-b57e-f85a8a3f113d · outbound

This paper cites Memory-Efficient LLM Training with Online Subspace Descent.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Memory-Efficient LLM Training with Online Subspace Descent

Reference 21

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Observation 7e82254b-2dae-417a-a908-44bf23178cdc · outbound

This paper cites Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training

Reference 22

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Observation 3e7889d9-cf31-4f79-99d8-4259ffa823c1 · outbound

This paper cites Rotate your networks: Better weight consolidation and less catastrophic forgetting.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Rotate your networks: Better weight consolidation and less catastrophic forgetting

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:40:43.077676Z

Source-reported events for the cited work

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Observation 029ceef8-ab5d-4aca-b05b-4e2f4b180344 · outbound

This paper cites Decoupled Weight Decay Regularization.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Decoupled Weight Decay Regularization

Reference 24

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Observation e8cf7dd5-e2d9-4a41-a976-cdaca4afda18 · outbound

This paper cites Hamiltonian Descent Methods.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Hamiltonian Descent Methods

Reference 25

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Observation eef32fcf-6c65-4a70-8af7-aff3bea8de8e · outbound

This paper cites New insights and perspectives on the natural gradient method.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization New insights and perspectives on the natural gradient method

Reference 26

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Observation e76b30be-105c-4f14-baba-1295bb6c8e89 · outbound

This paper cites Optimizing neural networks with kronecker-factored approximate curvature.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Optimizing neural networks with kronecker-factored approximate curvature

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:40:43.063560Z

Source-reported events for the cited work

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Observation 9bce9ba0-b666-4026-8659-30882c7e084f · outbound

This paper cites Memory-Efficient Optimization with Factorized Hamiltonian Descent.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Memory-Efficient Optimization with Factorized Hamiltonian Descent

Reference 28

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Observation ebfacd26-ae79-436e-bd40-bb24d510028c · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 29

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Observation 4c273979-9323-4434-b003-6b635919f17e · outbound

This paper cites On the Convergence of Adam and Beyond.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization On the Convergence of Adam and Beyond

Reference 30

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Observation 14d8ddb4-6d87-478f-9b8b-32a33b1b6277 · outbound

This paper cites GLU Variants Improve Transformer.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization GLU Variants Improve Transformer

Reference 31

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Observation 76ef8fd9-bd9d-4880-a84b-7cf82267a834 · outbound

This paper cites Adafactor: Adaptive learning rates with sublinear memory cost.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Adafactor: Adaptive learning rates with sublinear memory cost

Reference 32

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Observation c03ac2ed-81f4-4cef-8001-8964a1fe084f · outbound

This paper cites On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization On the Heavy-Tailed Theory of Stochastic Gradient Descent for Deep Neural Networks

Reference 33

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This paper cites On the Origin of Implicit Regularization in Stochastic Gradient Descent.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization On the Origin of Implicit Regularization in Stochastic Gradient Descent

Reference 34

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Observation aa28a64b-0392-4235-a35e-cf3d0223a4f9 · outbound

This paper cites Rethinking the inception architecture for computer vision.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Rethinking the inception architecture for computer vision

Reference 35

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Observation da615b9c-8a5f-450f-ac68-904309ce080b · outbound

This paper cites Recent advances in stochastic gradient descent in deep learning.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Recent advances in stochastic gradient descent in deep learning

Reference 36

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Observation 7b21e59c-053e-42b9-99a5-bfdbe53db156 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 37

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Observation 85f06c53-e8cb-48e6-8f05-71f606f0d17c · outbound

This paper cites SOAP: Improving and Stabilizing Shampoo using Adam.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization SOAP: Improving and Stabilizing Shampoo using Adam

Reference 38

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Observation c1c76ea2-26c3-4e6e-a6a4-a345fe1028d1 · outbound

This paper cites Root mean square layer normalization.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Root mean square layer normalization

Reference 39

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Observation c919e508-7103-45e9-9caa-f3bf0e7be8d6 · outbound

This paper cites mixup: Beyond Empirical Risk Minimization.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization mixup: Beyond Empirical Risk Minimization

Reference 40

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This paper cites Why are adaptive methods good for attention models? Advances in Neural Information Processing Systems, 33: 0 15383--15393, 2020.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Why are adaptive methods good for attention models? Advances in Neural Information Processing Systems, 33: 0 15383--15393, 2020

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:40:43.024078Z

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Observation f8377d3a-7a91-4b77-8ca3-1083b32cc39e · outbound

This paper cites Adam can converge without any modification on update rules.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Adam can converge without any modification on update rules

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:40:43.013528Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7a7411e0-c16f-4ab8-84f3-1b891eecdaa3 · outbound

This paper cites Why Transformers Need Adam: A Hessian Perspective.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Why Transformers Need Adam: A Hessian Perspective

Reference 43

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Observation 5d7ab80c-32a2-4b23-b766-31a4adc02603 · outbound

This paper cites GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-08T12:40:42.767627Z

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Observation 48ae89c5-412a-4f97-b9fe-72f49e1e412f · outbound

This paper cites Towards theoretically understanding why sgd generalizes better than adam in deep learning.

Improving Adaptive Moment Optimization via Preconditioner Diagonalization Towards theoretically understanding why sgd generalizes better than adam in deep learning

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T12:40:43.001783Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation e1060d59-b93e-4c5d-ab10-06d97084637f · inbound

Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations cites this paper.

Evolution of Optimization Methods: Algorithms, Scenarios, and Evaluations Improving Adaptive Moment Optimization via Preconditioner Diagonalization

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:51:21.226714Z

Source-reported events for the cited work

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