Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
And the Bit Goes Down: Revisiting the Quantization of Neural Networks
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1907.05686.
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 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T19:32:44.316534Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-04T22:40:49.053807Z
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 5fdfdc41-a20e-42bf-9f3f-03e04a9ad90d · inbound
VQ4ALL: Efficient Neural Network Representation via a Universal Codebook cites this paper.
VQ4ALL: Efficient Neural Network Representation via a Universal Codebook And the Bit Goes Down: Revisiting the Quantization of Neural Networks
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T19:32:44.316534Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2630ae3c-afa4-43c9-8d68-cad003903177 · inbound
MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization cites this paper.
MVQ:Towards Efficient DNN Compression and Acceleration with Masked Vector Quantization And the Bit Goes Down: Revisiting the Quantization of Neural Networks
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T16:17:52.989129Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5125e73e-7eed-4153-9fc9-343c640c1f2a · inbound
Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects cites this paper.
Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects And the Bit Goes Down: Revisiting the Quantization of Neural Networks
Reference 68
- Resolution
- verified exact
- local_arxiv, observed 2026-08-04T22:40:49.058787Z
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.