Paper Citation Record · LEDGER

Sparse Networks from Scratch: Faster Training without Losing Performance

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:1907.04840.

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

pith.paper-citation-record.v1
1907.04840 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T04:15:29.069142Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T20:55:03.877322Z

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 d44d0bd7-bf8f-4a77-aee8-b1febe115aa9 · inbound

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale cites this paper.

LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 15

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T13:35:36.064754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 092b9acf-139e-42e5-9168-c6922994809d · inbound

Advancing Weight and Channel Sparsification with Enhanced Saliency cites this paper.

Advancing Weight and Channel Sparsification with Enhanced Saliency Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-09T04:15:29.069142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61f28856-7849-4afc-b622-6ef12bb32ca3 · inbound

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling cites this paper.

NeuroTrails: Training with Dynamic Sparse Heads as the Key to Effective Ensembling Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:12.949488Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c7446cd7-0245-4e81-867b-7c8c54889992 · inbound

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum cites this paper.

Hyperpruning: Efficient Search through Pruned Variants of Recurrent Neural Networks Leveraging Lyapunov Spectrum Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T05:27:08.126151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d0450fb-c953-4cc6-b83f-f79876816ae7 · inbound

Hybrid Least Squares/Gradient Descent Methods for DeepONets cites this paper.

Hybrid Least Squares/Gradient Descent Methods for DeepONets Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T18:02:29.743385Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 412d8cbb-ef24-4f83-a906-6d48bd4cb4b1 · inbound

On a stochastic column-block bregman method for nonlinear systems cites this paper.

On a stochastic column-block bregman method for nonlinear systems Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:05:59.941105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b5c447b7-b381-46f2-b2be-f0943c5f12f7 · inbound

Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers cites this paper.

Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:30:55.854559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 786b751b-0065-4339-95c8-137f4cbf3828 · inbound

Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers cites this paper.

Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-19T18:13:10.053995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation adda466a-6d86-4e98-a4e5-2eb34a74c0a0 · inbound

Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers cites this paper.

Adaptive Regularization for Sparsity Control in Bregman-Based Optimizers Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:04:02.519702Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 074eab8e-02f4-4a74-a5be-74ff098f7efd · inbound

On the Stability of Growth in Structural Plasticity cites this paper.

On the Stability of Growth in Structural Plasticity Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-05-19T15:42:38.770072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation b5e63f17-24dd-4cbd-9e4d-39dd80351fc7 · inbound

On the Stability of Growth in Structural Plasticity cites this paper.

On the Stability of Growth in Structural Plasticity Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 8

Resolution: verified exact; arxiv_id, observed 2026-06-30T20:55:03.878944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a25ccc81-1e6d-494c-8c39-54ba69cbdc1b · inbound

Memory-Efficient LLM Training with Dynamic Sparsity: From Stability to Practical Scaling cites this paper.

Memory-Efficient LLM Training with Dynamic Sparsity: From Stability to Practical Scaling Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 49

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T19:42:36.006011Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 24fc2588-9a05-4684-ad76-0846ddec7a84 · inbound

Constrained Hebbian Learning Supports Efficient Representational Allocation under Structural Constraints cites this paper.

Constrained Hebbian Learning Supports Efficient Representational Allocation under Structural Constraints Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-01T21:39:31.513634Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4664cd13-8524-4b23-b102-b47e66835fe2 · inbound

Sparse Gaussian-Mixture-Model Q-Functions via Hadamard Overparametrization for Online Reinforcement Learning cites this paper.

Sparse Gaussian-Mixture-Model Q-Functions via Hadamard Overparametrization for Online Reinforcement Learning Sparse Networks from Scratch: Faster Training without Losing Performance

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-30T21:23:27.884957Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.