Paper Citation Record · LEDGER

Picking Winning Tickets Before Training by Preserving Gradient Flow

As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 31 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 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 31 of 31 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:36:02.915449Z

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 2026-08-15T06:32:42.880941+00:00.

Observation 31c0cf6e-1ca9-458f-8a91-ca2b8cbc455b · inbound

Electrostatic Force Regularization for Neural Structured Pruning cites this paper.

Electrostatic Force Regularization for Neural Structured Pruning Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T19:02:07.063054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f8c2a7d-36ae-4977-b971-bbb0c7f6a27d · inbound

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics cites this paper.

F$^3$OCUS -- Federated Finetuning of Vision-Language Foundation Models with Optimal Client Layer Updating Strategy via Multi-objective Meta-Heuristics Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-12T18:53:06.611584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 583b95d5-276b-4096-abb8-1bac657b6c8c · inbound

GradAlign for Training-free Model Performance Inference cites this paper.

GradAlign for Training-free Model Performance Inference Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-12T05:52:21.966912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af9557e3-cf43-4aac-86cc-4531ee094fdd · inbound

PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models cites this paper.

PTSBench: A Comprehensive Post-Training Sparsity Benchmark Towards Algorithms and Models Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-11T19:02:11.833676Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca4cf1ac-3f81-4bba-902a-ffc671b681d7 · inbound

Unified Stochastic Framework for Neural Network Quantization and Pruning cites this paper.

Unified Stochastic Framework for Neural Network Quantization and Pruning Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-11T05:04:32.497801Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eacf90a0-9af4-4706-a989-d6cc9d384976 · inbound

Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights cites this paper.

Vision Transformer Neural Architecture Search for Out-of-Distribution Generalization: Benchmark and Insights Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-10T21:50:16.620550Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64b813fb-67eb-4667-bd4d-bb8338ee31a5 · inbound

Pruning for Sparse Diffusion Models based on Gradient Flow cites this paper.

Pruning for Sparse Diffusion Models based on Gradient Flow Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T20:04:29.544583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation beebf16b-87b8-4200-a0c7-603fd51e5c8a · inbound

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

On Accelerating Edge AI: Optimizing Resource-Constrained Environments Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T14:46:38.213775Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7a7bac84-90d2-4f1c-9123-aa1d518af1f6 · inbound

Information Consistent Pruning: How to Efficiently Search for Sparse Networks? cites this paper.

Information Consistent Pruning: How to Efficiently Search for Sparse Networks? Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T14:14:41.883552Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74a5339b-c161-439e-a0d8-dcf8c6f9f0c9 · inbound

CoNNect: Connectivity-Based Regularization for Structural Pruning cites this paper.

CoNNect: Connectivity-Based Regularization for Structural Pruning Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-09T17:59:51.051949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 c2cdf459-e36a-4e44-b02a-dcff1ebfb25e · inbound

Revisiting LoRA through the Lens of Parameter Redundancy: Spectral Encoding Helps cites this paper.

Revisiting LoRA through the Lens of Parameter Redundancy: Spectral Encoding Helps Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 79

Resolution: unresolved; no resolver link, observed 2026-08-15T19:24:06.520562Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eabc2e1c-10ff-4028-8101-87f37864c167 · inbound

Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity cites this paper.

Sparse-Reg: Improving Sample Complexity in Offline Reinforcement Learning using Sparsity Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T19:14:53.750998Z

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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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 2026-08-15T06:32:42.880941+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.

Observation 433ac4e8-c2ef-4659-b650-61ac71103dbb · inbound

MOON: Multi-Objective OrthoNormalized Updates for Multitask Learning cites this paper.

MOON: Multi-Objective OrthoNormalized Updates for Multitask Learning Picking Winning Tickets Before Training by Preserving Gradient Flow

Reference 275

Resolution: unresolved; no resolver link, observed 2026-08-16T00:36:02.915449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.