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Paper Citation Record · LEDGER

FFCV: Accelerating Training by Removing Data Bottlenecks

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2306.12517.

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

pith.paper-citation-record.v1
2306.12517 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:34:48.818843Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-11T00:16:15.729268Z

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 d53d1295-7f8f-4960-b941-815b33392b87 · inbound

Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation cites this paper.

Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation FFCV: Accelerating Training by Removing Data Bottlenecks

Reference 78

Resolution
verified exact
local_arxiv, observed 2026-08-10T20:15:41.074153Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

source=arxiv_source observed=2026-08-10T20:15:40.052130Z digest=sha256:d4cf24229bb4d765fa4e1e9fb976661beabf8795b6afdaf1ad98369ffdffc5ce

Observation 54fdeb2f-13be-433c-b2de-5bb7139ea77f · inbound

EMLIO: Minimizing I/O Latency and Energy Consumption for Large-Scale AI Training cites this paper.

EMLIO: Minimizing I/O Latency and Energy Consumption for Large-Scale AI Training FFCV: Accelerating Training by Removing Data Bottlenecks

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-15T17:34:48.818843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T17:34:48.818843Z digest=sha256:a38eb474bae71573019c7b2c73636932cc2724c21d97bb5eca56c25f1fc11535