Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 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 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:52:04.208977Z

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
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

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 2026-08-08T06:32:00.761636+00:00.

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 2026-08-08T06:32:00.761636+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.