Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:18:23.157124Z
Paper Citation Record · LEDGER
Learning by solving differential equations
As of 21 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-10T18:59:08.087421Z
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
- parse uncertain0
- malformed identifier0
- metadata mismatch0
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:27.115884Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:18:22.462622Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.964886Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.868201Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.773425Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:18:22.495835Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1144f997-7b1a-4f97-a460-76939126bbca · outbound
This paper cites On Symplectic Optimization.
Learning by solving differential equations On Symplectic Optimization
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:18:22.502533Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1c4519c5-fc5d-4739-b1bd-4be0f93a4d7b · outbound
This paper cites an unresolved cited work.
Learning by solving differential equations Unresolved cited work
Reference 8
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T20:18:26.614998Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:18:22.514491Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9267039f-a465-4c74-a838-08dcc625da2e · outbound
This paper cites an unresolved cited work.
Learning by solving differential equations Unresolved cited work
Reference 10
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T20:18:26.597168Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:18:22.530022Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:18:22.536336Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.574556Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.479314Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:18:22.596538Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:18:22.609049Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.450800Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.407359Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T20:18:23.457767Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.357211Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.338224Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.321056Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.267017Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation a9ed09a9-fb5f-4f1d-b75f-1eb55743b672 · outbound
This paper cites Gilmer, George E.
Learning by solving differential equations Gilmer, George E
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.144894Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:18:22.680348Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 005d1f2f-5782-46d3-a6f0-80df2cc5d829 · outbound
This paper cites Geometric numerical integration.
Learning by solving differential equations Geometric numerical integration
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:18:22.686446Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:26.051326Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:25.891932Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:18:22.705987Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:25.837132Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:25.662218Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:18:22.728100Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7710a172-794c-456c-95c1-405b61a7971a · outbound
This paper cites Continuous time analysis of momentum methods.
Learning by solving differential equations Continuous time analysis of momentum methods
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:25.595776Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:25.504048Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
Observation d2300d01-58d7-4969-b0f9-0a08aaf9907e · outbound
This paper cites an unresolved cited work.
Learning by solving differential equations Unresolved cited work
Reference 36
- Resolution
- unresolved
- raw_fallback, observed 2026-08-15T20:18:25.428099Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:18:25.341088Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.
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
- Resolution
- 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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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.
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.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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.
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.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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.
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 2026-08-21T06:32:19.484+00:00.
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.
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 2026-08-21T06:32:19.484+00:00.
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 2026-08-21T06:32:19.484+00:00.
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
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- raw_fallback, observed 2026-08-15T20:18:24.330830Z
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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
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- 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 2026-08-21T06:32:19.484+00:00.
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
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- 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 2026-08-21T06:32:19.484+00:00.
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
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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
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- raw_fallback, observed 2026-08-15T20:18:24.014201Z
Source-reported events for the cited work
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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.
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.
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
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Unavailable: canonical work link unavailable.
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
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- raw_fallback, observed 2026-08-15T20:18:23.846012Z
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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
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- raw_fallback, observed 2026-08-15T20:18:23.732983Z
Source-reported events for the cited work
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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
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- raw_fallback, observed 2026-08-15T20:18:23.700864Z
Source-reported events for the cited work
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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
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- raw_fallback, observed 2026-08-15T20:18:23.660964Z
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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
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- arxiv_id, observed 2026-05-10T23:35:52.059328Z
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