Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
tf.data: A Machine Learning Data Processing Framework
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2101.12127.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T17:34:48.852204Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-22T21:15:09.603008Z
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 0ecf53de-5d5d-4554-aa1e-9b436101f1bf · inbound
Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput cites this paper.
Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput tf.data: A Machine Learning Data Processing Framework
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T14:14:35.334066Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c5c4b6bd-954d-4c59-9536-79047b68b25a · inbound
MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training cites this paper.
MegaScale-Data: Scaling Dataloader for Multisource Large Foundation Model Training tf.data: A Machine Learning Data Processing Framework
Reference 52
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T21:15:09.605303Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation f94cd173-0cda-4b02-b468-c66feb221964 · 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 tf.data: A Machine Learning Data Processing Framework
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:34:48.852204Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.