Paper Citation Record · LEDGER

When Deep Learning Meets Polyhedral Theory: A Survey

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2305.00241.

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

pith.paper-citation-record.v1
2305.00241 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T10:15:41.449979Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T11:09:11.653819Z

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 4351aa3d-1cc2-4784-8db4-3cc9f6247a18 · inbound

An analysis of optimization problems involving ReLU neural networks cites this paper.

An analysis of optimization problems involving ReLU neural networks When Deep Learning Meets Polyhedral Theory: A Survey

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-09T10:15:41.449979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 690244c6-5575-4482-a4f7-8160c1af102d · 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 When Deep Learning Meets Polyhedral Theory: A Survey

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-07T14:52:03.953651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0dc9e99-2208-47ff-87d6-059ec96054e4 · inbound

Global optimization of graph acquisition functions for neural architecture search cites this paper.

Global optimization of graph acquisition functions for neural architecture search When Deep Learning Meets Polyhedral Theory: A Survey

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:48:05.036677Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 69613b3c-2b20-4433-a9f6-f571771d4363 · inbound

Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees cites this paper.

Conformal Mixed-Integer Constraint Learning with Feasibility Guarantees When Deep Learning Meets Polyhedral Theory: A Survey

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-07T11:09:11.767778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.