Paper Citation Record · LEDGER

Universal Approximation Theorem for Deep Q-Learning via FBSDE System

As of 22 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2505.06023.

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

pith.paper-citation-record.v1
2505.06023 v1

Coverage vector

measured 24 of 24 reference resolution

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Source: paper_references, paper_reference_links, observed 2026-08-15T22:59:28.032556Z

measured 24 of 24 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Reference resolution

24 of 24 outbound references displayed

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External citation measurements

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Outbound references

Observation 2854fbeb-fb5b-4be9-907b-539389aed339 · outbound

This paper cites Universal approximation bounds for superpositions of a sigmoidal function.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Universal approximation bounds for superpositions of a sigmoidal function

Reference 1

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Source-reported events for the cited work

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Observation a4030a73-7320-4811-9faf-42e248632de7 · outbound

This paper cites Sparse grids.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Sparse grids

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.633077Z

Source-reported events for the cited work

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Observation 35f1b6c9-2fbb-4bd5-8f5d-6300d9123976 · outbound

This paper cites Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Universal approximation to nonlinear operators by neural networks with arbitrary activation functions and its application to dynamical systems

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.617778Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 19e23bf0-8766-4270-a5e3-7e55bb06af0b · outbound

This paper cites Approximation by superpositions of a sigmoidal function.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Approximation by superpositions of a sigmoidal function

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:59:27.691713Z

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Observation b9df00fe-5670-4e61-b345-495467f33b65 · outbound

This paper cites Backward stochastic differential equations in finance.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Backward stochastic differential equations in finance

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.592974Z

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Observation cac49589-26f8-40a5-9e87-a4832237a91e · outbound

This paper cites Controlled Markov processes and viscosity solutions, volume 25.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Controlled Markov processes and viscosity solutions, volume 25

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.554924Z

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 fb2183c1-b7f6-4c59-bbd5-e5c04739bc48 · outbound

This paper cites Tensor spaces and numerical tensor calculus, volume 42.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Tensor spaces and numerical tensor calculus, volume 42

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.469585Z

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 aabf6e2f-312a-4ef9-95c4-c393fc0dd5ba · outbound

This paper cites Deep residual learning for image recognition.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Deep residual learning for image recognition

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T22:59:27.713900Z

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Observation bacfaa2b-3237-4e15-ace3-3202709461b4 · outbound

This paper cites Approximation capabilities of multilayer feedforward networks.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Approximation capabilities of multilayer feedforward networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:59:27.718223Z

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Observation b62d4ffc-9b28-47c4-8330-9ca0d20ac08f · outbound

This paper cites General topology.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System General topology

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.400856Z

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 b3dd384a-525f-4ffd-8d02-b3ca55e388b2 · outbound

This paper cites Neural operator: Learning maps between function spaces with applications to pdes.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Neural operator: Learning maps between function spaces with applications to pdes

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T22:59:27.727213Z

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Observation 4cb52aae-f600-499a-98b6-58a8613c2158 · outbound

This paper cites Nonlinear elliptic and parabolic equations of the second order.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Nonlinear elliptic and parabolic equations of the second order

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.376018Z

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 c1442566-f04d-430c-8807-56767f2152ff · outbound

This paper cites Deep learning via dynamical systems: An approximation perspective.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Deep learning via dynamical systems: An approximation perspective

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.360218Z

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 8fa42e65-5374-4642-b3dc-027f2da49974 · outbound

This paper cites Fourier neural operator for parametric partial differential equations.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Fourier neural operator for parametric partial differential equations

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T22:59:27.741611Z

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Unavailable: canonical work link unavailable.

Observation e6b4c52d-7f68-4cd2-926d-6db4010d6c5f · outbound

This paper cites Learning nonlinear operators via deeponet based on the universal approximation theorem of operators.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Learning nonlinear operators via deeponet based on the universal approximation theorem of operators

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T22:59:27.750220Z

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Observation 08e702d2-95d0-4740-b860-557727060170 · outbound

This paper cites Forward-backward stochastic differential equations and their applications.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Forward-backward stochastic differential equations and their applications

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.323641Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e2bad422-8c09-4d00-9144-6302e904fe64 · outbound

This paper cites Human-level control through deep reinforcement learning.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Human-level control through deep reinforcement learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.303836Z

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 3c715032-ec88-467e-ba04-bfc6319cd93b · outbound

This paper cites Stochastic differential equations: an introduction with applications.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Stochastic differential equations: an introduction with applications

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.283608Z

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 28dd248d-dc5e-4263-b8c7-68f07d7fa3b1 · outbound

This paper cites Bsdes, weak convergence and homogenization of semilinear pdes.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Bsdes, weak convergence and homogenization of semilinear pdes

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.234111Z

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 0d80ec48-7dff-47fb-8bf4-21d7af874066 · outbound

This paper cites Stochastic hamilton--jacobi--bellman equations.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Stochastic hamilton--jacobi--bellman equations

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.155310Z

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 78cf89fe-d3a6-4a2d-9d7e-467e3ea9ad4b · outbound

This paper cites Universal Approximation Theorem of Deep Q-Networks.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Universal Approximation Theorem of Deep Q-Networks

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-15T22:59:28.075347Z

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 a7446acc-beec-4d43-b346-d3f8497f072d · outbound

This paper cites A mean-field optimal control formulation of deep learning.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System A mean-field optimal control formulation of deep learning

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.115518Z

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 9c850e5f-3673-488b-9c2d-b95d5800ea32 · outbound

This paper cites Error bounds for approximations with deep relu networks.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Error bounds for approximations with deep relu networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T22:59:28.028068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3db277f3-b9c9-4535-9194-26a10cf7f158 · outbound

This paper cites Stochastic controls: Hamiltonian systems and HJB equations, volume 43 of Applications of Mathematics.

Universal Approximation Theorem for Deep Q-Learning via FBSDE System Stochastic controls: Hamiltonian systems and HJB equations, volume 43 of Applications of Mathematics

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:59:28.089957Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.