Paper Citation Record · LEDGER

Picking Winning Tickets Before Training by Preserving Gradient Flow

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:2002.07376.

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

pith.paper-citation-record.v1
2002.07376 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 18 of 18 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 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:49:36.288557Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T08:16:48.019661Z

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 1cbfc631-4d76-4da8-b919-9e4aa0053e2c · inbound

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation cites this paper.

SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 170

Resolution: verified exact; arxiv_id, observed 2026-05-16T17:56:23.622163Z

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 c860b926-b457-490b-8064-136cd630760d · inbound

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights cites this paper.

Training-free Neural Architecture Search through Variance of Knowledge of Deep Network Weights Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-08T20:49:36.288557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80bc991e-b43c-4815-ab48-766998a16114 · inbound

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption cites this paper.

Efficient Privacy-Preserving Cross-Silo Federated Learning with Multi-Key Homomorphic Encryption Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T15:34:37.639754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 609ad148-c3bc-42f5-9ba5-dd3c5f57f4aa · 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 Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T14:44:21.715735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea803949-0526-4e7c-9a97-99374dd142f1 · inbound

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning cites this paper.

PacTrain: Pruning and Adaptive Sparse Gradient Compression for Efficient Collective Communication in Distributed Deep Learning Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-07T14:33:09.091356Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a75551b0-eea2-4f65-b1af-8522cd30e42a · 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 Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T05:27:08.102709Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e7fd8b2d-45e8-4de9-ad77-f721bf722307 · inbound

Dynamic Sparse Training of Diagonally Sparse Networks cites this paper.

Dynamic Sparse Training of Diagonally Sparse Networks Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T04:15:17.025375Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 097eb9b6-9752-4214-ba26-09b33d081714 · 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 Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T04:50:33.511032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9b71dcf-fa57-483c-9ff7-06ddaa2c2f24 · inbound

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts cites this paper.

Exploring Sparse Adapters for Scalable Merging of Parameter Efficient Experts Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T19:07:36.919868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de031a25-1e16-4682-881b-efdf1ad903b5 · inbound

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

Efficient Column-Wise N:M Pruning on RISC-V CPU Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T14:55:28.441400Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f93a18b3-5865-4e9d-9df6-a2574931afde · 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 Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 150

Resolution: unresolved; no resolver link, observed 2026-08-05T22:03:09.880445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5c017746-2f3a-4eff-8a96-9e499b1f7067 · inbound

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models cites this paper.

EGGS-PTP: An Expander-Graph Guided Structured Post-training Pruning Method for Large Language Models Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T21:08:18.710324Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1acd5da7-b604-4fff-b307-e5b9d83e948c · inbound

Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach cites this paper.

Engineering Resource-constrained Software Systems with DNN Components: a Concept-based Pruning Approach Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 92

Resolution: verified exact; arxiv_id, observed 2026-05-11T08:20:59.216348Z

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 39c6b556-1841-4003-aea3-dd023609d4b5 · 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 Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 86

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T06:17:23.193607Z

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 91644d8b-8b40-487b-8a22-a3e29dd7ea0d · inbound

Can We Predict The Human Preference For Text-to-Image Content Prior To Generation And Is It Even Useful To Do So? cites this paper.

Can We Predict The Human Preference For Text-to-Image Content Prior To Generation And Is It Even Useful To Do So? Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 66

Resolution: verified exact; arxiv_id, observed 2026-07-02T08:16:48.021191Z

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 d1ac8f57-f191-486a-bdd6-683e58ba9776 · inbound

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs cites this paper.

RQP: Resource-Oriented Quantiser Pruning for Neural Networks on FPGAs Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-07-01T15:25:47.438055Z

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 d240a51a-5447-45a5-87d6-0ae3a2ce8b32 · inbound

Double-Scoring: Reliable Extraction of Strong Lottery Tickets cites this paper.

Double-Scoring: Reliable Extraction of Strong Lottery Tickets Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T02:25:33.138417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8787a990-056b-48cf-9e1c-1db6dca4a204 · inbound

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts cites this paper.

Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 100

Resolution: unresolved; no resolver link, observed 2026-08-01T16:42:15.153700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.