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Paper Citation Record · LEDGER

Learning by solving differential equations

As of 20 August 2026, this Paper Citation Record lists 68 of 68 outbound references and 1 inbound Pith citation observation for arXiv:2505.13397.

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

pith.paper-citation-record.v1
2505.13397 v1

Coverage vector

measured 68 of 68 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:18:23.157124Z

measured 69 of 69 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T18:59:08.087421Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T23:35:52.053423Z

Reference resolution

68 of 68 outbound references displayed

  • verified exact3
  • verified fuzzy39
  • unresolved26
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 03ff58f4-532d-42e4-9463-dbc88d8e310d · outbound

This paper cites Natural gradient works efficiently in learning.Neural computation, 10(2):251– 276, 1998.

Learning by solving differential equations Natural gradient works efficiently in learning.Neural computation, 10(2):251– 276, 1998

Reference 1

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Observation 66a905be-88ab-4cea-ac70-eaf68543fd31 · outbound

This paper cites Scalable Second Order Optimization for Deep Learning.

Learning by solving differential equations Scalable Second Order Optimization for Deep Learning

Reference 2

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Observation 968aa42d-18f9-4ede-bc35-327e4a18d8bd · outbound

This paper cites Stochastic runge-kutta methods and adaptive sgd-g2 stochastic gradient descent.

Learning by solving differential equations Stochastic runge-kutta methods and adaptive sgd-g2 stochastic gradient descent

Reference 3

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Observation fece649c-6fff-4736-b912-aca9a8ecd5e9 · outbound

This paper cites Liapunov Functions and Stability in Control Theory.

Learning by solving differential equations Liapunov Functions and Stability in Control Theory

Reference 4

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Observation 6027926c-3884-42ec-805c-850d9d679cdf · outbound

This paper cites Barrett and Benoit Dherin.

Learning by solving differential equations Barrett and Benoit Dherin

Reference 5

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a961ea67-c89f-43cf-98c2-d5e6ab5e16ad · outbound

This paper cites Modular Duality in Deep Learning.

Learning by solving differential equations Modular Duality in Deep Learning

Reference 6

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Observation 1144f997-7b1a-4f97-a460-76939126bbca · outbound

This paper cites On Symplectic Optimization.

Learning by solving differential equations On Symplectic Optimization

Reference 7

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Observation 1c4519c5-fc5d-4739-b1bd-4be0f93a4d7b · outbound

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Learning by solving differential equations Unresolved cited work

Reference 8

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 5954805a-8ba4-47a8-92a0-f26812abb3d0 · outbound

This paper cites On the Implicit Bias of Adam.

Learning by solving differential equations On the Implicit Bias of Adam

Reference 9

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Observation 9267039f-a465-4c74-a838-08dcc625da2e · outbound

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Learning by solving differential equations Unresolved cited work

Reference 10

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Observation 65de98a4-5126-4390-a8a0-f687433b8724 · outbound

This paper cites Benchmarking Neural Network Training Algorithms.

Learning by solving differential equations Benchmarking Neural Network Training Algorithms

Reference 11

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Observation b5a52664-5c03-405c-a1e1-c5e739163673 · outbound

This paper cites The Road Less Scheduled.

Learning by solving differential equations The Road Less Scheduled

Reference 12

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Observation 4dff64c8-755a-4cd9-874d-00be4f54c548 · outbound

This paper cites Why neural networks find simple solutions: The many regularizers of geometric complexity.

Learning by solving differential equations Why neural networks find simple solutions: The many regularizers of geometric complexity

Reference 13

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 41d13a17-e9dd-4397-bd9d-931f91cbf094 · outbound

This paper cites Corridor geometry in gradient-based optimization, 2024.

Learning by solving differential equations Corridor geometry in gradient-based optimization, 2024

Reference 14

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Observation 02d38264-98ed-43b8-8142-41b91a35acd0 · outbound

This paper cites Adam: A method for stochastic optimization.

Learning by solving differential equations Adam: A method for stochastic optimization

Reference 15

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Observation be601e57-6a6b-4741-99a1-7306b9e29c5c · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Learning by solving differential equations An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 16

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Observation 9f8b9bcb-0b4c-49d9-8070-f17ff1e9ab26 · outbound

This paper cites Incorporating nesterov momentum into adam.

Learning by solving differential equations Incorporating nesterov momentum into adam

Reference 17

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 62e6ef9f-0f5c-4a8a-9227-6ba6fcb67dc8 · outbound

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

Learning by solving differential equations Adaptive subgradient methods for online learning and stochastic optimization

Reference 18

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation ff67d33b-0a82-4bd0-9c5d-0c1394632215 · outbound

This paper cites Towards Hyperparameter-Agnostic DNN Training via Dynamical System Insights.

Learning by solving differential equations Towards Hyperparameter-Agnostic DNN Training via Dynamical System Insights

Reference 19

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Source-reported events for the cited work

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Observation e02f8997-fa1c-405f-b915-d8b49ac6576a · outbound

This paper cites Conformal symplectic and relativistic optimization.

Learning by solving differential equations Conformal symplectic and relativistic optimization

Reference 20

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Observation a1d4824e-4f46-493c-9250-23cefa338b9e · outbound

This paper cites Admm and accelerated admm as continuous dynamical systems.

Learning by solving differential equations Admm and accelerated admm as continuous dynamical systems

Reference 21

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Observation 81aa9ba0-673d-492f-ac36-30866188db16 · outbound

This paper cites Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization.

Learning by solving differential equations Gradient flows and proximal splitting methods: A unified view on accelerated and stochastic optimization

Reference 22

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Observation b352cc6e-6dbe-4308-a0e4-8512149143ce · outbound

This paper cites Implicit regularization in heavy-ball momentum accelerated stochastic gradient descent.

Learning by solving differential equations Implicit regularization in heavy-ball momentum accelerated stochastic gradient descent

Reference 23

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Observation a9ed09a9-fb5f-4f1d-b75f-1eb55743b672 · outbound

This paper cites Gilmer, George E.

Learning by solving differential equations Gilmer, George E

Reference 24

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation b380030d-8c46-4d52-a068-fe44ae6bed73 · outbound

This paper cites Shampoo: Preconditioned stochastic tensor optimization.

Learning by solving differential equations Shampoo: Preconditioned stochastic tensor optimization

Reference 25

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Observation 005d1f2f-5782-46d3-a6f0-80df2cc5d829 · outbound

This paper cites Geometric numerical integration.

Learning by solving differential equations Geometric numerical integration

Reference 26

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Observation 825d7c72-d1d5-4de2-b98d-f99c87701c55 · outbound

This paper cites Solving Ordinary Differential Equations I: Nonstiff Problems, volume 8 of Springer Series in Computational Mathematics.

Learning by solving differential equations Solving Ordinary Differential Equations I: Nonstiff Problems, volume 8 of Springer Series in Computational Mathematics

Reference 27

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Observation da5fbaa1-8257-432a-a4ce-7b6647e9484d · outbound

This paper cites Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems, volume 14 of Springer Series in Computational Mathematics.

Learning by solving differential equations Solving Ordinary Differential Equations II: Stiff and Differential-Algebraic Problems, volume 14 of Springer Series in Computational Mathematics

Reference 28

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verified fuzzy
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Observation 8fb229f3-2c41-4d97-b68d-2b2d287b3ec5 · outbound

This paper cites Deep residual learning for image recognition.

Learning by solving differential equations Deep residual learning for image recognition

Reference 30

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Unavailable: canonical work link unavailable.

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Observation bb5452b8-6cf0-4fd8-84f0-df47d6ae182d · outbound

This paper cites Optax: composable gradient transformation and optimisation, in jax!, 2020.

Learning by solving differential equations Optax: composable gradient transformation and optimisation, in jax!, 2020

Reference 31

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Source-reported events for the cited work

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Observation 6ca52cf0-208f-4658-a2a5-81b9e43458cf · outbound

This paper cites Yang, Zachary Nado, Sourabh Medapati, Philipp Hennig, Michael Rabbat, and George E.

Learning by solving differential equations Yang, Zachary Nado, Sourabh Medapati, Philipp Hennig, Michael Rabbat, and George E

Reference 32

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation ccae3fc9-fd20-4f4a-b14d-fe7a295c9eb2 · outbound

This paper cites Kingma and Jimmy Ba.

Learning by solving differential equations Kingma and Jimmy Ba

Reference 33

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Unavailable: canonical work link unavailable.

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This paper cites Continuous time analysis of momentum methods.

Learning by solving differential equations Continuous time analysis of momentum methods

Reference 34

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Source-reported events for the cited work

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Observation f51f446d-5990-429f-92e2-df3c769212fd · outbound

This paper cites Learning multiple layers of features from tiny images.

Learning by solving differential equations Learning multiple layers of features from tiny images

Reference 35

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Source-reported events for the cited work

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Observation d2300d01-58d7-4969-b0f9-0a08aaf9907e · outbound

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Learning by solving differential equations Unresolved cited work

Reference 36

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Observation 6d440c03-489e-4881-87ce-0da4730b3a1e · outbound

This paper cites Visualizing the loss landscape of neural nets.

Learning by solving differential equations Visualizing the loss landscape of neural nets

Reference 37

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verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation a080bbe2-1ff5-411b-b18c-48fc7dc8612d · outbound

This paper cites Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks.

Learning by solving differential equations Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks

Reference 38

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verified exact
local_arxiv, observed 2026-08-15T20:18:23.434886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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Observation 21f9ccff-3735-4c1f-aaf9-e0f38d913326 · outbound

This paper cites Understanding the difficulty of training transformers.

Learning by solving differential equations Understanding the difficulty of training transformers

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:25.324462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.758099Z digest=sha256:55c9589c678ba8e2265e5cbe539e0312c9b2ed277798cebf25ce39247762cc7c

Observation 60c78eab-7265-494d-bad3-c9a333464496 · outbound

This paper cites Decoupled Weight Decay Regularization.

Learning by solving differential equations Decoupled Weight Decay Regularization

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-15T20:18:22.763757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:18:22.763757Z digest=sha256:42af43540d44bf67318f4f364a6220fbd41c6f5f0e46c860a782251e85340d8b

Observation 1a5dcf76-a5f6-41d3-8184-2bc2e92a9d50 · outbound

This paper cites Decoupled weight decay regularization.

Learning by solving differential equations Decoupled weight decay regularization

Reference 41

Resolution
unresolved
no resolver link, observed 2026-08-15T20:18:22.769025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:18:22.769025Z digest=sha256:b0141877bb3a65824984bcb35824717659ad2a9c145604b52c33039a135b0c12

Observation 0fe631f6-533f-4922-91aa-518e67826b17 · outbound

This paper cites Aggregated momentum: Stability through passive damping.

Learning by solving differential equations Aggregated momentum: Stability through passive damping

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:25.130972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.773979Z digest=sha256:b48cb6aee252b6846acf962ec1fbe61219245f2c308613f4d8d0ba72197e0240

Observation e867cff2-9c37-4fc3-9944-ee29e09f1303 · outbound

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

Learning by solving differential equations Optimizing neural networks with kronecker-factored approx- imate curvature

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:25.091802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.779905Z digest=sha256:57e93eb5a530a4197da80ae97ae710f1419b06bc0e7fd8ff52ac0e2675f92cb1

Observation 73e701cf-276a-4022-85a7-d8bf1afe6abf · outbound

This paper cites An Empirical Model of Large-Batch Training.

Learning by solving differential equations An Empirical Model of Large-Batch Training

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-15T20:18:22.784730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:18:22.784730Z digest=sha256:52d2ee6fd7019eebeb438c9962649c7bc295f54b9e72446829261cc607b888e4

Observation 92dac4d5-cfa1-41a4-846f-7049edd2814e · outbound

This paper cites A dynamical systems perspective on nesterov acceleration.

Learning by solving differential equations A dynamical systems perspective on nesterov acceleration

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:25.060613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.790085Z digest=sha256:3d3dcd42a9b77d6ccd09d12ebea2c54efe788b938e05cee7829394df325af6f3

Observation 69e15b79-db64-494c-aced-63dec3dd6178 · outbound

This paper cites Dynamics of sgd with stochastic polyak stepsizes: Truly adaptive variants and convergence to exact solution.

Learning by solving differential equations Dynamics of sgd with stochastic polyak stepsizes: Truly adaptive variants and convergence to exact solution

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:25.013224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.795738Z digest=sha256:58717774b0e3960082e9465dfb8d8f7ab832738be62782a80ce9a5fa4378e59f

Observation 32608d8a-48ce-452f-8f8e-de7ac191902b · outbound

This paper cites An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes.

Learning by solving differential equations An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes

Reference 47

Resolution
verified exact
local_arxiv, observed 2026-08-15T20:18:23.371801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.801326Z digest=sha256:c009f52acefb2791894c975795a9469641422e81d6dcc02a674d656abfff8b32

Observation 65e8e5e7-7ab2-4c42-a946-f9f4a720007a · outbound

This paper cites an unresolved cited work.

Learning by solving differential equations Unresolved cited work

Reference 48

Resolution
unresolved
no resolver link, observed 2026-08-15T20:18:22.806754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:18:22.806754Z digest=sha256:1eea66af86b9a151f2b08d8ead404878ec3d41d140d2049151a655eb58bbafdd

Observation a039634c-082a-46fc-91bc-a3b3e2466a4d · outbound

This paper cites On the curvature of the loss landscape.

Learning by solving differential equations On the curvature of the loss landscape

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-15T20:18:22.818487Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:18:22.818487Z digest=sha256:568b3b7052b78af87dc80f60ddd165f738fb11905fc7f7205e8d20eb1190afcb

Observation 208203fc-063a-4209-b90f-bd5f8aedeb41 · outbound

This paper cites Training generative adversarial networks by solving ordinary differential equations.

Learning by solving differential equations Training generative adversarial networks by solving ordinary differential equations

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:24.805055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.825290Z digest=sha256:aed407f51a593a4b462c8ed97c7f3a066a40464402921beb36ee84c4e83e573c

Observation 4793e944-c548-4cd8-b2a9-874e8e2f6435 · outbound

This paper cites an unresolved cited work.

Learning by solving differential equations Unresolved cited work

Reference 51

Resolution
unresolved
raw_fallback, observed 2026-08-15T20:18:24.735471Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.830636Z digest=sha256:c9fdc6d010e898604d3e6d6565a61503f1632435316d40149c708a426d5a9c0b

Observation dce8106a-5a2a-4131-a0ed-16311e8d0802 · outbound

This paper cites On a continuous time model of gradient descent dynamics and instability in deep learning.

Learning by solving differential equations On a continuous time model of gradient descent dynamics and instability in deep learning

Reference 52

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:24.714675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.838255Z digest=sha256:8ac1db4e18355778905a47b8be102a7325b6a5fcaef309f482784af024b1ff13

Observation bcab1e9b-fb10-4a76-8bd8-08460345daaf · outbound

This paper cites Rumelhart, Geoffrey E.

Learning by solving differential equations Rumelhart, Geoffrey E

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-15T20:18:22.850188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:18:22.850188Z digest=sha256:06cde04fafe8f4218b3b9044a7e468ccf26f2acbe47ec5111c86794affa4004c

Observation 7da91aca-d7b6-4b8d-a42c-32dc4107e708 · outbound

This paper cites Acceleration via symplectic discretiza- tion of high-resolution differential equations.

Learning by solving differential equations Acceleration via symplectic discretiza- tion of high-resolution differential equations

Reference 54

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:24.588064Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.858319Z digest=sha256:0166837fe6669b9fb351988a2db3feeae9aaad9d2cd86d58eb767adcc59597ad

Observation 774ec820-ea2a-42c4-9cb4-74490d2ff5f8 · outbound

This paper cites A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale.

Learning by solving differential equations A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-15T20:18:22.869640Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:18:22.869640Z digest=sha256:d0339110be758c1d364911ff0fd7162705d5911eeedc439e45c4d34d659b74cb

Observation 5868b212-3cfe-4715-bf46-8d654632addb · outbound

This paper cites Improving optimizers by runge-kutta method: A case study of sgd and adam.

Learning by solving differential equations Improving optimizers by runge-kutta method: A case study of sgd and adam

Reference 56

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:24.547762Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.875960Z digest=sha256:0f5d54515d378a9210ac0c33fd6ae0a8765adb3cc8dd184d0c7bd40eee827c5a

Observation 8584df0f-d513-4458-9908-f29a12901bdb · outbound

This paper cites A differential equation for modeling nes- terov’s accelerated gradient method: theory and insights.Journal of Machine Learning Research, 17:1–43, 2016.

Learning by solving differential equations A differential equation for modeling nes- terov’s accelerated gradient method: theory and insights.Journal of Machine Learning Research, 17:1–43, 2016

Reference 57

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:24.347360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.880941Z digest=sha256:8857483ca8633d7ac12ff562c3390063b07883194b228cb3e83143894214118f

Observation 430f1dd9-6eb2-48c8-bb4a-8fecc4cf9925 · outbound

This paper cites On the importance of initial- ization and momentum in deep learning.

Learning by solving differential equations On the importance of initial- ization and momentum in deep learning

Reference 58

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:24.330830Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.885742Z digest=sha256:f7c27d82dc89f14ed0284dc01e0015ccbd0f0bf3801156f09d245dd6a7488a6e

Observation 82bcacb4-e48a-491a-9d34-f2b1c28d41ef · outbound

This paper cites Spike no more: Stabilizing the pre-training of large language models, 2025.

Learning by solving differential equations Spike no more: Stabilizing the pre-training of large language models, 2025

Reference 59

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:24.313927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:22.932623Z digest=sha256:9b9ef3b530ff45037b6a707d3bd331ffa6c8ed6758609b19425dbe22d0c40b81

Observation d5bae36d-cbaf-45b7-b1ae-1ea8afb4afe0 · outbound

This paper cites Rmsprop: Divide the gradient by a running average of its recent magnitude.

Learning by solving differential equations Rmsprop: Divide the gradient by a running average of its recent magnitude

Reference 60

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:24.164832Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:23.013615Z digest=sha256:7d65b70a4ea44368ceddc3fe07e59bfdb3acb8b6bfdc09b20b4d996c0fc388f4

Observation d92ae326-9fa7-4c84-af77-c998b112ff03 · outbound

This paper cites Understanding and mitigating gradient flow pathologies in physics-informed neural networks.

Learning by solving differential equations Understanding and mitigating gradient flow pathologies in physics-informed neural networks

Reference 61

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:24.084746Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:23.086544Z digest=sha256:c74f52c3ee27f6635bff96ee9adb9cd129b109027258a638619f66f4e99bc1e2

Observation 88d6b176-d1f3-4c4d-bbe4-029cefcfebe0 · outbound

This paper cites Small-scale proxies for large-scale transformer training instabilities.

Learning by solving differential equations Small-scale proxies for large-scale transformer training instabilities

Reference 62

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:24.014201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:23.122833Z digest=sha256:25ee13aa00075377925179990a5841143c21d1502f802a09917f4227881ba88c

Observation 8a680dac-4912-4dee-b999-c4a336e1a62a · outbound

This paper cites Structured Preconditioners in Adaptive Optimization: A Unified Analysis.

Learning by solving differential equations Structured Preconditioners in Adaptive Optimization: A Unified Analysis

Reference 63

Resolution
unresolved
no resolver link, observed 2026-08-15T20:18:23.127819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:18:23.127819Z digest=sha256:e0ceec95823d8bba7e9f707696e9714f107eeec637ac571948eb33ce49f93ad3

Observation 17d3b554-adc8-4394-9c8f-a9a4c4c2344c · outbound

This paper cites Wide Residual Networks.

Learning by solving differential equations Wide Residual Networks

Reference 64

Resolution
unresolved
no resolver link, observed 2026-08-15T20:18:23.132324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:18:23.132324Z digest=sha256:3ce7b679b809a2d7095a339317058731dd2075b6a24b822e9ccb6300f45af71a

Observation 193ccf0a-bb3b-40a9-bb77-6e202b44a1de · outbound

This paper cites ADADELTA: An Adaptive Learning Rate Method.

Learning by solving differential equations ADADELTA: An Adaptive Learning Rate Method

Reference 65

Resolution
unresolved
no resolver link, observed 2026-08-15T20:18:23.136809Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:18:23.136809Z digest=sha256:983e565a1fa89c969d6dc20e7b79ed9be9ab38c602a8439c35fcd0b8ea5adb2e

Observation 80ae47f4-847e-41f2-a022-00f993663354 · outbound

This paper cites Direct runge-kutta discretiza- tion achieves acceleration.

Learning by solving differential equations Direct runge-kutta discretiza- tion achieves acceleration

Reference 66

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:23.846012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:23.142424Z digest=sha256:87ada61c785183d5e3b8540cdd032850eb16dc946ceb88032a48f0fd34525057

Observation 3046b463-b1e2-4d26-a58f-ed605b9dc44f · outbound

This paper cites Lookahead optimizer: k steps forward, 1 step back.

Learning by solving differential equations Lookahead optimizer: k steps forward, 1 step back

Reference 67

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:23.732983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:23.147237Z digest=sha256:ae9e339db2e8ed278174434ab2c0debaa968c5ecc4419d16b91347938fb9a67e

Observation 6125e1de-952e-45f7-9c37-fa49cc82570c · outbound

This paper cites In the main paper, we benchmarked RK4, which is the classical 4th order method, and it has an error of sizeO(h5).

Learning by solving differential equations In the main paper, we benchmarked RK4, which is the classical 4th order method, and it has an error of sizeO(h5)

Reference 68

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:23.700864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:23.151672Z digest=sha256:b6d764561986887facf19e67b6ce796eacf8a020053fb13901a3735a0cb0c767

Observation 9145da29-6785-4019-a8f4-76a610a7d4b7 · outbound

This paper cites Therefore, the correct way to write the gradient flow ODE is with the help of an underlying metric tensorG(θ) on the parameter space given by ˙θ(t) = G(θ(t))−1∇L(θ(t)).

Learning by solving differential equations Therefore, the correct way to write the gradient flow ODE is with the help of an underlying metric tensorG(θ) on the parameter space given by ˙θ(t) = G(θ(t))−1∇L(θ(t))

Reference 69

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:18:23.660964Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:18:23.157124Z digest=sha256:715e3c4ec3fab4177a35327e78a3167f42df5f4525d48a281a93544dd6d49874

Pith citing papers

Observation 80868c1c-b999-4999-9040-dd58d0d0c9f4 · inbound

FlowAdam: Implicit Regularization via Geometry-Aware Soft Momentum Injection cites this paper.

FlowAdam: Implicit Regularization via Geometry-Aware Soft Momentum Injection Learning by solving differential equations

Reference 18

Resolution
verified exact
arxiv_id, observed 2026-05-10T23:35:52.059328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-10T18:59:08.087421Z digest=sha256:b38a3db43b04708462acb25752f19acebcb0bb7a06807f4804a94ef5237537f9