Paper Citation Record · LEDGER

A Signal Propagation Perspective for Pruning Neural Networks at Initialization

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:1906.06307.

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

pith.paper-citation-record.v1
1906.06307 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:08:52.018582Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T17:29:59.486052Z

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 84c1bb11-555d-4917-99a4-1cfa43096ac6 · inbound

Pruning Deep Convolutional Neural Network Using Conditional Mutual Information cites this paper.

Pruning Deep Convolutional Neural Network Using Conditional Mutual Information A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-12T11:08:52.018582Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a782c686-6983-488e-9877-effc0e2409f3 · inbound

On Accelerating Edge AI: Optimizing Resource-Constrained Environments cites this paper.

On Accelerating Edge AI: Optimizing Resource-Constrained Environments A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.262450Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ee37d8b0-cbe7-4647-997d-88c63b97fbf9 · inbound

Accelerating Diffusion Transformer via Error-Optimized Cache cites this paper.

Accelerating Diffusion Transformer via Error-Optimized Cache A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T20:53:59.327859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7d7043c7-c5ec-4e9a-9bad-a8f3009e7d1d · 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 A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T05:27:08.099294Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59d43b14-6dba-4866-bc5a-48bc6318a5bb · inbound

Towards Universal & Efficient Model Compression via Exponential Torque Pruning cites this paper.

Towards Universal & Efficient Model Compression via Exponential Torque Pruning A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 1989

Resolution: unresolved; no resolver link, observed 2026-08-06T22:17:43.313565Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation adc4c753-1de6-4343-a4ba-4d565c7aadda · inbound

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study cites this paper.

Investigating Structural Pruning and Recovery Techniques for Compressing Multimodal Large Language Models: An Empirical Study A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T13:22:45.868130Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed2bd15f-915b-4887-b686-1229afda6e5c · inbound

Low Latency GNN Accelerator for Quantum Error Correction cites this paper.

Low Latency GNN Accelerator for Quantum Error Correction A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-15T00:48:24.995864Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e183a7fe-653a-4b69-b2d2-635329d92058 · inbound

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond cites this paper.

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 210

Resolution: verified exact; arxiv_id, observed 2026-05-11T19:26:08.020534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 41155e45-8943-4c32-928e-0342fe73db64 · inbound

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond cites this paper.

Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 210

Resolution: verified exact; arxiv_id, observed 2026-07-04T17:29:59.487563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 09a4a171-cc9d-4ae4-b081-23ce607ed789 · inbound

Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency cites this paper.

Structural Pruning of Large Vision Language Models: A Comprehensive Study on Pruning Dynamics, Recovery, and Data Efficiency A Signal Propagation Perspective for Pruning Neural Networks at Initialization

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T21:56:13.655975Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.