Paper Citation Record · LEDGER

Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2102.00554.

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

pith.paper-citation-record.v1
2102.00554 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:23:54.961291Z

measured 0 of 1 external citation measurements

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 2026-08-20T06:33:59.587034+00:00.

Observation 1711f637-1a21-424a-8050-0b95a30920b5 · inbound

Learning Fine-grained Parameter Sharing via Sparse Tensor Decomposition cites this paper.

Learning Fine-grained Parameter Sharing via Sparse Tensor Decomposition Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T20:22:43.635783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 859608bf-5a03-4ca8-b2e0-76f0d2347722 · inbound

EfQAT: An Efficient Framework for Quantization-Aware Training cites this paper.

EfQAT: An Efficient Framework for Quantization-Aware Training Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-12T19:06:18.373901Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b13c496-6edc-426a-aa14-c61eeca701f7 · inbound

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting cites this paper.

A Comparative Study of Pruning Methods in Transformer-based Time Series Forecasting Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-11T13:41:20.954942Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6b30469a-92a9-486f-9e01-622f22d7fd88 · inbound

Switch-Based Multi-Part Neural Network cites this paper.

Switch-Based Multi-Part Neural Network Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T10:23:54.961291Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e705a8df-cbd6-465d-9acd-b932edb491a6 · inbound

Thoughts on Objectives of Sparse and Hierarchical Masked Image Model cites this paper.

Thoughts on Objectives of Sparse and Hierarchical Masked Image Model Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T22:11:14.974503Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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.