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Paper Citation Record · LEDGER

Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2104.05158.

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

pith.paper-citation-record.v1
2104.05158 v7

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T04:55:01.973832Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T04:59:46.207134Z

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 9d5bb313-75c6-4286-8ff4-27edca4f021b · inbound

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel cites this paper.

PyTorch FSDP: Experiences on Scaling Fully Sharded Data Parallel Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Reference 20

Resolution
metadata mismatch
arxiv_id, observed 2026-05-12T04:15:20.090082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T04:15:20.027659Z digest=sha256:683afc2700097c0e35d890c6673feed0da544834c6ac6031ce868e3bca039eda

Observation 90f30e1d-f538-41f0-9023-7287b4b95032 · inbound

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:06:27.985285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T04:33:41.411292Z digest=sha256:bc37c3f97a0849b115681ed4abcf44a5dda7b0e441d4f4c2f2506deed7958405

Observation e715c2f5-58a5-4000-81e2-fa357bc2a8b2 · inbound

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale cites this paper.

LoKA: Low-precision Kernel Applications for Recommendation Models At Scale Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models

Reference 53

Resolution
verified exact
arxiv_id, observed 2026-05-15T04:59:46.210306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-15T04:55:01.973832Z digest=sha256:ad9fe5d91d76e491837b435756e586590b52f7729afa9b5a9843a6697ecb41a8