Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2102.00554.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:04:35.229822Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T17:18:35.222737Z
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 531bdc5b-dc44-4293-bbba-00c1c14d9005 · inbound
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers cites this paper.
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T17:18:35.226075Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.
Observation 54b4bd74-e7e3-43d7-8592-8198178ab1e0 · inbound
TriADA: Massively Parallel Trilinear Matrix-by-Tensor Multiply-Add Algorithm and Device Architecture for the Acceleration of 3D Discrete Transformations cites this paper.
TriADA: Massively Parallel Trilinear Matrix-by-Tensor Multiply-Add Algorithm and Device Architecture for the Acceleration of 3D Discrete Transformations Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:04:35.229822Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.