Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:18:23.157124Z
Paper Citation Record · LEDGER
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.
Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:18:23.157124Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+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
68 of 68 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 03ff58f4-532d-42e4-9463-dbc88d8e310d · outbound
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
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
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
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
Learning by solving differential equations Barrett and Benoit Dherin
Reference 5
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Observation a961ea67-c89f-43cf-98c2-d5e6ab5e16ad · outbound
Learning by solving differential equations Modular Duality in Deep Learning
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Observation 1144f997-7b1a-4f97-a460-76939126bbca · outbound
Learning by solving differential equations On Symplectic Optimization
Reference 7
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Reference 8
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Observation 5954805a-8ba4-47a8-92a0-f26812abb3d0 · outbound
Learning by solving differential equations On the Implicit Bias of Adam
Reference 9
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Observation 9267039f-a465-4c74-a838-08dcc625da2e · outbound
Learning by solving differential equations Unresolved cited work
Reference 10
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Observation 65de98a4-5126-4390-a8a0-f687433b8724 · outbound
Learning by solving differential equations Benchmarking Neural Network Training Algorithms
Reference 11
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Observation b5a52664-5c03-405c-a1e1-c5e739163673 · outbound
Learning by solving differential equations The Road Less Scheduled
Reference 12
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Observation 4dff64c8-755a-4cd9-874d-00be4f54c548 · outbound
Learning by solving differential equations Why neural networks find simple solutions: The many regularizers of geometric complexity
Reference 13
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Observation 41d13a17-e9dd-4397-bd9d-931f91cbf094 · outbound
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
Learning by solving differential equations Adam: A method for stochastic optimization
Reference 15
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Observation be601e57-6a6b-4741-99a1-7306b9e29c5c · outbound
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
Learning by solving differential equations Incorporating nesterov momentum into adam
Reference 17
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Observation 62e6ef9f-0f5c-4a8a-9227-6ba6fcb67dc8 · outbound
Learning by solving differential equations Adaptive subgradient methods for online learning and stochastic optimization
Reference 18
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Observation ff67d33b-0a82-4bd0-9c5d-0c1394632215 · outbound
Learning by solving differential equations Towards Hyperparameter-Agnostic DNN Training via Dynamical System Insights
Reference 19
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Observation e02f8997-fa1c-405f-b915-d8b49ac6576a · outbound
Learning by solving differential equations Conformal symplectic and relativistic optimization
Reference 20
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Observation a1d4824e-4f46-493c-9250-23cefa338b9e · outbound
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
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
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
Learning by solving differential equations Gilmer, George E
Reference 24
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Observation b380030d-8c46-4d52-a068-fe44ae6bed73 · outbound
Learning by solving differential equations Shampoo: Preconditioned stochastic tensor optimization
Reference 25
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Observation 005d1f2f-5782-46d3-a6f0-80df2cc5d829 · outbound
Learning by solving differential equations Geometric numerical integration
Reference 26
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Observation 825d7c72-d1d5-4de2-b98d-f99c87701c55 · outbound
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
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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Observation 8fb229f3-2c41-4d97-b68d-2b2d287b3ec5 · outbound
Learning by solving differential equations Deep residual learning for image recognition
Reference 30
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Observation bb5452b8-6cf0-4fd8-84f0-df47d6ae182d · outbound
Learning by solving differential equations Optax: composable gradient transformation and optimisation, in jax!, 2020
Reference 31
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Observation 6ca52cf0-208f-4658-a2a5-81b9e43458cf · outbound
Learning by solving differential equations Yang, Zachary Nado, Sourabh Medapati, Philipp Hennig, Michael Rabbat, and George E
Reference 32
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Observation ccae3fc9-fd20-4f4a-b14d-fe7a295c9eb2 · outbound
Learning by solving differential equations Kingma and Jimmy Ba
Reference 33
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Observation 7710a172-794c-456c-95c1-405b61a7971a · outbound
Learning by solving differential equations Continuous time analysis of momentum methods
Reference 34
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Observation f51f446d-5990-429f-92e2-df3c769212fd · outbound
Learning by solving differential equations Learning multiple layers of features from tiny images
Reference 35
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Observation d2300d01-58d7-4969-b0f9-0a08aaf9907e · outbound
Learning by solving differential equations Unresolved cited work
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Observation 6d440c03-489e-4881-87ce-0da4730b3a1e · outbound
Learning by solving differential equations Visualizing the loss landscape of neural nets
Reference 37
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Observation a080bbe2-1ff5-411b-b18c-48fc7dc8612d · outbound
Learning by solving differential equations Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks
Reference 38
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Observation 21f9ccff-3735-4c1f-aaf9-e0f38d913326 · outbound
Learning by solving differential equations Understanding the difficulty of training transformers
Reference 39
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Observation 60c78eab-7265-494d-bad3-c9a333464496 · outbound
Learning by solving differential equations Decoupled Weight Decay Regularization
Reference 40
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Observation 1a5dcf76-a5f6-41d3-8184-2bc2e92a9d50 · outbound
Learning by solving differential equations Decoupled weight decay regularization
Reference 41
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Observation 0fe631f6-533f-4922-91aa-518e67826b17 · outbound
Learning by solving differential equations Aggregated momentum: Stability through passive damping
Reference 42
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Observation e867cff2-9c37-4fc3-9944-ee29e09f1303 · outbound
Learning by solving differential equations Optimizing neural networks with kronecker-factored approx- imate curvature
Reference 43
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Observation 73e701cf-276a-4022-85a7-d8bf1afe6abf · outbound
Learning by solving differential equations An Empirical Model of Large-Batch Training
Reference 44
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Observation 92dac4d5-cfa1-41a4-846f-7049edd2814e · outbound
Learning by solving differential equations A dynamical systems perspective on nesterov acceleration
Reference 45
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Observation 69e15b79-db64-494c-aced-63dec3dd6178 · outbound
Learning by solving differential equations Dynamics of sgd with stochastic polyak stepsizes: Truly adaptive variants and convergence to exact solution
Reference 46
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Observation 32608d8a-48ce-452f-8f8e-de7ac191902b · outbound
Learning by solving differential equations An Adaptive Stochastic Gradient Method with Non-negative Gauss-Newton Stepsizes
Reference 47
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Observation 65e8e5e7-7ab2-4c42-a946-f9f4a720007a · outbound
Learning by solving differential equations Unresolved cited work
Reference 48
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Observation a039634c-082a-46fc-91bc-a3b3e2466a4d · outbound
Learning by solving differential equations On the curvature of the loss landscape
Reference 49
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Learning by solving differential equations Training generative adversarial networks by solving ordinary differential equations
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Observation 4793e944-c548-4cd8-b2a9-874e8e2f6435 · outbound
Learning by solving differential equations Unresolved cited work
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Observation dce8106a-5a2a-4131-a0ed-16311e8d0802 · outbound
Learning by solving differential equations On a continuous time model of gradient descent dynamics and instability in deep learning
Reference 52
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Observation bcab1e9b-fb10-4a76-8bd8-08460345daaf · outbound
Learning by solving differential equations Rumelhart, Geoffrey E
Reference 53
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Observation 7da91aca-d7b6-4b8d-a42c-32dc4107e708 · outbound
Learning by solving differential equations Acceleration via symplectic discretiza- tion of high-resolution differential equations
Reference 54
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Learning by solving differential equations A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scale
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Learning by solving differential equations Improving optimizers by runge-kutta method: A case study of sgd and adam
Reference 56
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Observation 8584df0f-d513-4458-9908-f29a12901bdb · outbound
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
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Learning by solving differential equations On the importance of initial- ization and momentum in deep learning
Reference 58
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Learning by solving differential equations Spike no more: Stabilizing the pre-training of large language models, 2025
Reference 59
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Learning by solving differential equations Rmsprop: Divide the gradient by a running average of its recent magnitude
Reference 60
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Learning by solving differential equations Understanding and mitigating gradient flow pathologies in physics-informed neural networks
Reference 61
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Observation 88d6b176-d1f3-4c4d-bbe4-029cefcfebe0 · outbound
Learning by solving differential equations Small-scale proxies for large-scale transformer training instabilities
Reference 62
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Learning by solving differential equations Structured Preconditioners in Adaptive Optimization: A Unified Analysis
Reference 63
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Learning by solving differential equations Wide Residual Networks
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Learning by solving differential equations ADADELTA: An Adaptive Learning Rate Method
Reference 65
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Learning by solving differential equations Direct runge-kutta discretiza- tion achieves acceleration
Reference 66
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Learning by solving differential equations Lookahead optimizer: k steps forward, 1 step back
Reference 67
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Observation 6125e1de-952e-45f7-9c37-fa49cc82570c · outbound
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
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Observation 9145da29-6785-4019-a8f4-76a610a7d4b7 · outbound
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
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Observation 80868c1c-b999-4999-9040-dd58d0d0c9f4 · inbound
FlowAdam: Implicit Regularization via Geometry-Aware Soft Momentum Injection Learning by solving differential equations
Reference 18
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