Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-03T14:48:00.784384Z
Paper Citation Record · LEDGER
As of 8 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 0 inbound Pith citation observations for arXiv:2512.19409.
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 2026-08-03T14:48:00.784384Z
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
38 of 38 outbound references displayed
External citation measurements
No source-named external measurement is stored.
Observation 63e123aa-c270-4b9f-b148-12422cad7e62 · outbound
Symplectic Representation of Legendre Dynamics Methods of information geometry , volume 191
Reference 1
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Observation 4f5b66e8-eb8a-47ae-a405-5a895ef343a1 · outbound
Symplectic Representation of Legendre Dynamics Mathematical methods of classical mechanics , volume 60
Reference 2
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Observation af854ae1-170f-40fd-8ede-b55c9678868d · outbound
Symplectic Representation of Legendre Dynamics On invariance and selectivity in representation learning
Reference 3
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Observation f6cedc63-a0f1-4909-83f3-663fd88270bf · outbound
Symplectic Representation of Legendre Dynamics Representation learning: A review and new perspectives
Reference 4
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Observation b2e9f0c3-2204-43f3-84aa-ed7dc71d1a1c · outbound
Symplectic Representation of Legendre Dynamics On explaining the surprising success of reservoir computing forecaster of chaos? the universal machine learning dynamical system with contrast to var and dmd
Reference 5
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Observation 23fa1969-6ad6-41b4-bc4c-25e35228846d · outbound
Symplectic Representation of Legendre Dynamics Projecting the Fokker-Planck Equation onto a finite dimensional exponential family
Reference 6
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Observation 4c7e277f-a03d-479e-a6bc-7bc714118844 · outbound
Symplectic Representation of Legendre Dynamics Lectures on the Geometry of Quantization , volume 8
Reference 7
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Observation d2f91c74-ec02-4b67-89fb-a9cab773d7c7 · outbound
Symplectic Representation of Legendre Dynamics Group equivariant convolutional networks
Reference 8
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Observation 7183fcbf-5abd-4f89-a2a1-c0e4e43ab2ac · outbound
Symplectic Representation of Legendre Dynamics Symplectic Recurrent Neural Networks
Reference 9
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Observation 259dcb13-d57f-47a8-bf0e-cc88fe9e441c · outbound
Symplectic Representation of Legendre Dynamics Information processing capacity of dynamical systems
Reference 10
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Observation 1e0611c4-4643-4682-a8d1-956667cb559a · outbound
Symplectic Representation of Legendre Dynamics Universality of real minimal complexity reservoir
Reference 11
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Observation 4b6595a0-1491-4165-9def-0f49e0780463 · outbound
Symplectic Representation of Legendre Dynamics Linear simple cycle reservoirs at the edge of stability perform fourier decomposition of the input driving signals
Reference 12
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Observation 33e8aa4c-ee5a-4984-963b-d0b97a0f47ac · outbound
Symplectic Representation of Legendre Dynamics Euler state networks: Non-dissipative reservoir computing
Reference 13
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Observation 7d66a134-dc5e-4e98-af48-a00bb3260443 · outbound
Symplectic Representation of Legendre Dynamics Echo state networks are universal
Reference 14
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Observation 529f006c-4bd5-4186-b63c-d77a221489eb · outbound
Symplectic Representation of Legendre Dynamics Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems
Reference 15
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Observation 63b24ba1-33eb-446d-a0bf-33a972ed894d · outbound
Symplectic Representation of Legendre Dynamics Reservoir computing universality with stochastic inputs
Reference 16
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Observation 62a93db4-3a8b-4399-b363-0d9107c660f3 · outbound
Symplectic Representation of Legendre Dynamics Kalman filtering and smoothing solutions to temporal gaussian process regression models
Reference 17
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Observation aeb77a5f-c226-4ad1-b0fb-94bfc030514e · outbound
Symplectic Representation of Legendre Dynamics Reservoir computing beyond memory-nonlinearity trade-off
Reference 18
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Observation 95c489a7-1333-410d-aadb-e268f364cf90 · outbound
Symplectic Representation of Legendre Dynamics Short term memory in echo state networks
Reference 19
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Observation 4c942d37-ff81-45ac-b5d0-1dd0a2c2ba66 · outbound
Reference 20
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Observation 60f16439-2a35-4c6a-8b8b-045930677ca2 · outbound
Symplectic Representation of Legendre Dynamics Tutorial on training recurrent neural networks, covering bppt, rtrl, ekf and the echo state network approach
Reference 21
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Observation 911cc78a-96e4-41d5-a325-19b974ee9106 · outbound
Symplectic Representation of Legendre Dynamics Harnessing nonlinearity: Predicting chaotic systems and saving energy in wireless communication
Reference 22
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Observation 045ea248-3a07-4fae-8b95-4d7c62053493 · outbound
Symplectic Representation of Legendre Dynamics An introduction to probabilistic graphical models, 2003
Reference 23
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Observation daa5b118-8768-4ae4-b11c-94dff37bc1cf · outbound
Symplectic Representation of Legendre Dynamics Markov-modulated affine processes
Reference 24
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Observation 0f99b053-0bef-4caf-9f4f-249c9a2519c5 · outbound
Symplectic Representation of Legendre Dynamics On translation invariance in cnns: Convolutional layers can exploit absolute spatial location
Reference 25
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Observation a400ea4e-bc67-49b1-a208-689b94082411 · outbound
Symplectic Representation of Legendre Dynamics Metric learning: A survey
Reference 26
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Observation e7fb5cd0-8a8d-47ee-9034-68059427a4e6 · outbound
Symplectic Representation of Legendre Dynamics Introduction to smooth manifolds
Reference 27
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Observation 7b0158d2-8f93-4a72-ab66-9ca64af1fdfd · outbound
Symplectic Representation of Legendre Dynamics Simple Cycle Reservoirs are Universal
Reference 28
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Observation 3d57361f-1247-46fe-a84d-f7884c534b0c · outbound
Symplectic Representation of Legendre Dynamics Reservoir computing approaches to recurrent neural network training
Reference 29
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Observation 26e18586-2611-4d0e-9cce-170ac60d13a6 · outbound
Symplectic Representation of Legendre Dynamics Real-time computing without stable states: A new framework for neural computation based on perturbations
Reference 30
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Observation 01d6a0ac-ba56-4b1f-a4ce-d24fe683cd8c · outbound
Symplectic Representation of Legendre Dynamics Stochastic processes and applications
Reference 31
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Observation f611d4e0-74cb-44ad-90cf-f5a01ddc52af · outbound
Symplectic Representation of Legendre Dynamics Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Reference 32
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Observation 1a56b45c-7d6a-417c-8e88-06f69c041397 · outbound
Symplectic Representation of Legendre Dynamics Minimum complexity echo state network
Reference 33
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Observation b99ceb4a-9ccc-4aef-b291-bc307e3384bf · outbound
Symplectic Representation of Legendre Dynamics Bayesian filtering and smoothing , volume 17
Reference 34
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Observation abfe06c8-4893-4b28-8abe-7eb0f1fd1ebc · outbound
Symplectic Representation of Legendre Dynamics Predicting the future of discrete sequences from fractal representations of the past
Reference 35
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Observation 1f95d7ea-46eb-43c5-a4a8-ee313e66b603 · outbound
Symplectic Representation of Legendre Dynamics Dynamical systems as temporal feature spaces
Reference 36
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Observation 638aa0f6-ee47-4290-8e6c-59281f48d650 · outbound
Symplectic Representation of Legendre Dynamics Universal Time-Series Representation Learning: A Survey
Reference 37
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Observation a7ab4038-d19a-42db-ab1f-be022f69dc8c · outbound
Symplectic Representation of Legendre Dynamics Recent advances in physical reservoir computing: A review
Reference 38
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No inbound Pith citation observations are available.