Paper Citation Record · LEDGER

Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2310.20360.

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

pith.paper-citation-record.v1
2310.20360 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T04:31:21.306388Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T07:29:38.364967Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 00ce966b-f4a9-4885-88bd-26079d1bdb30 · inbound

Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations cites this paper.

Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 47

Resolution: verified exact; arxiv_id, observed 2026-05-24T09:39:17.274470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 30443328-0964-4b8b-ac3e-dadebb730afd · inbound

An overview of diffusion models for generative artificial intelligence cites this paper.

An overview of diffusion models for generative artificial intelligence Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T04:31:21.306388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e687832c-1d29-4b78-ad6b-a209da229f8a · inbound

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry cites this paper.

Reconciling Privacy and Explainability in High-Stakes: A Systematic Inquiry Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-10T23:15:09.222527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 167a86b5-e6ab-48cc-97bf-4c292dda0a46 · inbound

High-Order Tensor Regression in Sparse Convolutional Neural Networks cites this paper.

High-Order Tensor Regression in Sparse Convolutional Neural Networks Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-10T22:39:23.124503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d5aa027-4488-4e57-bf8f-51aabce7b661 · inbound

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems cites this paper.

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T21:11:06.746262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23c3f78c-f589-4871-bc95-8213a9807e5f · inbound

Mathematical analysis of the gradients in deep learning cites this paper.

Mathematical analysis of the gradients in deep learning Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T14:10:55.184143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ef6e92aa-2f32-4663-92ba-263e2cd384fc · inbound

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time cites this paper.

Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:04.208977Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8916bea2-64ea-48d1-b5c0-54fa55f3d09e · inbound

PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning cites this paper.

PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T13:20:47.254649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f34331f-46fe-4adf-9bc2-3c20bd422fc8 · inbound

Survey on Methods for Detection, Classification and Location of Faults in Power Systems Using Artificial Intelligence cites this paper.

Survey on Methods for Detection, Classification and Location of Faults in Power Systems Using Artificial Intelligence Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 105

Resolution: unresolved; no resolver link, observed 2026-08-06T17:44:54.836682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f3a3157-953d-461a-b865-f2fe2d53000e · inbound

Central limit theorem for the averaged Adam optimizer cites this paper.

Central limit theorem for the averaged Adam optimizer Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-07-04T07:29:38.366617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0a1c98c6-e820-483a-ac35-9628850a6fb0 · inbound

Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks cites this paper.

Unified convergence analysis for gradient descent optimization methods in the training of deep neural networks Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-11T20:46:05.467029Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5cb91c23-7554-41a4-861b-7deacdb74107 · inbound

On MUON optimization: From non-convergence to an error analysis with Polar Express and the Newton-Schulz polynomial from implementations cites this paper.

On MUON optimization: From non-convergence to an error analysis with Polar Express and the Newton-Schulz polynomial from implementations Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-06T21:05:46.034470Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ff51282-77ad-4621-9f75-4559e1ebf49b · inbound

Walk-on-Spheres Monte Carlo and deep neural network approximations of elliptic PDEs with drift and killing cites this paper.

Walk-on-Spheres Monte Carlo and deep neural network approximations of elliptic PDEs with drift and killing Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T16:36:54.872612Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.