Paper Citation Record · LEDGER

Pruning neural networks without any data by iteratively conserving synaptic flow

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2006.05467.

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

pith.paper-citation-record.v1
2006.05467 v3

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 2026-08-07T06:34:17.273281+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:50:33.396828Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

75
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 868df59b-5cf2-450e-a9f4-4b271d1413e8 · inbound

Searching Efficient Deep Architectures for Radar Target Detection using Monte-Carlo Tree Search cites this paper.

Searching Efficient Deep Architectures for Radar Target Detection using Monte-Carlo Tree Search Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:50:33.396828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 828e290c-4146-411d-aa1b-b22b4f845db9 · inbound

Efficient Column-Wise N:M Pruning on RISC-V CPU cites this paper.

Efficient Column-Wise N:M Pruning on RISC-V CPU Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T14:55:28.436138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0b6bef41-c4bd-4e4f-af24-b00ba500cd7f · inbound

SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning cites this paper.

SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 141

Resolution: unresolved; no resolver link, observed 2026-08-05T22:03:09.861416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b98958b-5a28-4db8-8903-7b102b416c37 · inbound

Heterogeneous Connectivity in Sparse Networks: Fan-in Profiles, Gradient Hierarchy, and Topological Equilibria cites this paper.

Heterogeneous Connectivity in Sparse Networks: Fan-in Profiles, Gradient Hierarchy, and Topological Equilibria Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:16:01.318271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 974f6a07-b6ec-4dbe-bced-572803e2617c · inbound

Man, Machine, and Mathematics cites this paper.

Man, Machine, and Mathematics Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:51:27.783905Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 73648cb8-ede6-4c29-b9bc-4c45d214487f · inbound

XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity cites this paper.

XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:56:17.950231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation ce8196d5-902f-4ce9-a740-0aef95a98160 · inbound

XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity cites this paper.

XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:39:47.262686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 728598be-c2a3-4d8a-9596-42ba7360df30 · inbound

Not How Many, But Which: Parameter Placement in Low-Rank Adaptation cites this paper.

Not How Many, But Which: Parameter Placement in Low-Rank Adaptation Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 80

Resolution: verified exact; arxiv_id, observed 2026-05-13T06:17:23.161921Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b381cfd0-1f3d-4436-b5e6-210234ae7a17 · inbound

Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate cites this paper.

Theoretical Analysis of Sparse Optimization with Reparameterization, Weight Decay, and Adaptive Learning Rate Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 81

Resolution: verified exact; arxiv_id, observed 2026-06-30T11:54:38.484306Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d08197e2-e059-47c3-a5f3-372e9c1a1a16 · inbound

Channel Location Constrains the Auditability of Subliminal Learning cites this paper.

Channel Location Constrains the Auditability of Subliminal Learning Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-07-04T08:19:44.335780Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.