Paper Citation Record · LEDGER

Low-Memory Neural Network Training: A Technical Report

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1904.10631.

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

pith.paper-citation-record.v1
1904.10631 v2

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 2026-08-14T06:32:32.682623+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-14T05:29:24.456318Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-19T07:52:09.187133Z

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 8bae7354-1574-43c1-95bd-0f57871b5a84 · inbound

On the Downstream Performance of Compressed Word Embeddings cites this paper.

On the Downstream Performance of Compressed Word Embeddings Low-Memory Neural Network Training: A Technical Report

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-14T05:29:24.456318Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d8cd242-cb6a-49dd-8b1f-94f7aaa4cc13 · inbound

Reformer: The Efficient Transformer cites this paper.

Reformer: The Efficient Transformer Low-Memory Neural Network Training: A Technical Report

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-13T07:22:03.276331Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation b8c4ce52-6bce-42a8-84d4-b04d6bc089a4 · inbound

Fine-tuning Whisper on Low-Resource Languages for Real-World Applications cites this paper.

Fine-tuning Whisper on Low-Resource Languages for Real-World Applications Low-Memory Neural Network Training: A Technical Report

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-11T11:12:55.649855Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c968568b-da2d-4c22-8a6d-6eb31d3dd92b · inbound

Adjoint sharding for very long context training of state space models cites this paper.

Adjoint sharding for very long context training of state space models Low-Memory Neural Network Training: A Technical Report

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-10T22:50:28.775364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db303787-df38-4f3d-be76-5a742f634dff · inbound

Seeing World Dynamics in a Nutshell cites this paper.

Seeing World Dynamics in a Nutshell Low-Memory Neural Network Training: A Technical Report

Reference 56

Resolution: unresolved; no resolver link, observed 2026-08-09T04:42:06.477720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a80a344-a195-4cc9-9383-ee04aa23ca60 · inbound

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models cites this paper.

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models Low-Memory Neural Network Training: A Technical Report

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-07T13:54:40.107433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5910d50-20a1-41cd-b9b4-1740ad097057 · inbound

Geminet: Learning the Duality-based Iterative Process for Lightweight Traffic Engineering in Changing Topologies cites this paper.

Geminet: Learning the Duality-based Iterative Process for Lightweight Traffic Engineering in Changing Topologies Low-Memory Neural Network Training: A Technical Report

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-05-19T07:52:09.189445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.