Paper Citation Record · LEDGER

prunAdag: an adaptive pruning-aware gradient method

As of 9 August 2026, this Paper Citation Record lists 48 of 48 outbound references and 0 inbound Pith citation observations for arXiv:2502.08308.

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

pith.paper-citation-record.v1
2502.08308 v1

Coverage vector

measured 48 of 48 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T05:44:07.648913Z

measured 48 of 48 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

48 of 48 outbound references displayed

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verified fuzzy32
unresolved15
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0a6c2c69-6ad9-4664-98e9-bac7390f8e08 · outbound

This paper cites Global sparse momentum SGD for pruning very deep neural networks,.

prunAdag: an adaptive pruning-aware gradient method Global sparse momentum SGD for pruning very deep neural networks,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.167193Z

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Observation 48ed2c98-5c22-4a78-8e03-3655ab758e03 · outbound

This paper cites Compression-aware Training of Neural Networks using Frank-Wolfe.

prunAdag: an adaptive pruning-aware gradient method Compression-aware Training of Neural Networks using Frank-Wolfe

Reference 2

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Observation 49470e74-0a91-4559-840a-aba5ade95bf9 · outbound

This paper cites Adaptive subgradient methods for online learning and stochastic optimization.,.

prunAdag: an adaptive pruning-aware gradient method Adaptive subgradient methods for online learning and stochastic optimization.,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.157447Z

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Observation 432ac540-6f2f-450c-8396-04a9614edcdb · outbound

This paper cites Adaptive Bound Optimization for Online Convex Optimization.

prunAdag: an adaptive pruning-aware gradient method Adaptive Bound Optimization for Online Convex Optimization

Reference 4

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Observation a0014507-ac26-47e3-80e5-f19f12b7ad8a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

prunAdag: an adaptive pruning-aware gradient method Adam: A Method for Stochastic Optimization

Reference 5

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Observation bdd03782-02f8-4ae0-897a-75da1f43b71e · outbound

This paper cites Lecture 6.5-rmsprop, course ra: Neural networks for machine learning,.

prunAdag: an adaptive pruning-aware gradient method Lecture 6.5-rmsprop, course ra: Neural networks for machine learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.147687Z

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Observation aad9f289-83a1-411e-a2f1-9a278b8531e0 · outbound

This paper cites ADADELTA: An Adaptive Learning Rate Method.

prunAdag: an adaptive pruning-aware gradient method ADADELTA: An Adaptive Learning Rate Method

Reference 7

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Observation 6bdd45c5-518f-468a-8611-0c59bcb7a5ba · outbound

This paper cites Complexity and performance for two classes of noise-tolerant first-order algorithms.

prunAdag: an adaptive pruning-aware gradient method Complexity and performance for two classes of noise-tolerant first-order algorithms

Reference 8

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Observation 34f6a788-84ae-4e88-9182-f005e8163730 · outbound

This paper cites Multilevel objective-function-free optimization with an application to neural networks training,.

prunAdag: an adaptive pruning-aware gradient method Multilevel objective-function-free optimization with an application to neural networks training,

Reference 9

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Observation 8440792b-70f5-413a-b447-9dd655f03290 · outbound

This paper cites Complexity of a cl ass of ﬁrst-order objective-function-free opti- mization algorithms,.

prunAdag: an adaptive pruning-aware gradient method Complexity of a cl ass of ﬁrst-order objective-function-free opti- mization algorithms,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.125401Z

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Observation 78249044-402e-4964-8ee6-46f2036ae5cf · outbound

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prunAdag: an adaptive pruning-aware gradient method Unresolved cited work

Reference 11

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Observation ff8a1a47-819d-49cd-a8e4-97518204c849 · outbound

This paper cites Recent advances in trust region algorithm s,.

prunAdag: an adaptive pruning-aware gradient method Recent advances in trust region algorithm s,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.103570Z

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Observation 787b9975-38fb-400e-b12e-5a70843ef853 · outbound

This paper cites Pruning algorithms-a survey,.

prunAdag: an adaptive pruning-aware gradient method Pruning algorithms-a survey,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.093269Z

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Observation 1cd4e7fb-f461-4e5b-9cf1-7b840d29e43c · outbound

This paper cites Optimal brain damage ,.

prunAdag: an adaptive pruning-aware gradient method Optimal brain damage ,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.083582Z

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Observation 59539711-9cbf-4173-bdf4-7354fa744c21 · outbound

This paper cites To prune, or not to prune: exploring the efficacy of pruning for model compression.

prunAdag: an adaptive pruning-aware gradient method To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 15

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Observation 963adfb2-efe8-447a-930a-869eb453067e · outbound

This paper cites Accelerating very de ep convolutional networks for classiﬁcation and detection,.

prunAdag: an adaptive pruning-aware gradient method Accelerating very de ep convolutional networks for classiﬁcation and detection,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.074191Z

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Observation 234e9ae3-c3ed-4920-ab13-3a9b1a135d76 · outbound

This paper cites Exploiting linear structure within con- volutional networks for eﬃcient evaluation,.

prunAdag: an adaptive pruning-aware gradient method Exploiting linear structure within con- volutional networks for eﬃcient evaluation,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.064900Z

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Observation a5bbed32-994f-4c13-8f2f-83ee80e74778 · outbound

This paper cites On compressing deep mo dels by low rank and sparse decomposi- tion,.

prunAdag: an adaptive pruning-aware gradient method On compressing deep mo dels by low rank and sparse decomposi- tion,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.055279Z

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Observation 782d0a6d-1ed7-4899-a272-4b7cb3bc47c0 · outbound

This paper cites Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1.

prunAdag: an adaptive pruning-aware gradient method Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 19

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Observation c73fc56c-5894-484b-b002-0099341f2f0b · outbound

This paper cites T wo-step quantization for low-bit neural networks,.

prunAdag: an adaptive pruning-aware gradient method T wo-step quantization for low-bit neural networks,

Reference 20

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Observation 1de44bc3-29a1-4a72-850d-c57998208ecd · outbound

This paper cites Position-based scaled grad ient for model quantization and pruning,.

prunAdag: an adaptive pruning-aware gradient method Position-based scaled grad ient for model quantization and pruning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.035736Z

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Observation 82dbd911-f05c-4667-aace-d4313a2b208f · outbound

This paper cites Learning both wei ghts and connections for eﬃcient neural network,.

prunAdag: an adaptive pruning-aware gradient method Learning both wei ghts and connections for eﬃcient neural network,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.026480Z

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Observation a8bf48b1-27cb-42b9-8ea1-d22ea80a8864 · outbound

This paper cites Exploiting sparsene ss in deep neural networks for large vocabulary speech recognition,.

prunAdag: an adaptive pruning-aware gradient method Exploiting sparsene ss in deep neural networks for large vocabulary speech recognition,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.015428Z

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Observation d1923a57-1c27-41a9-bda4-fbd8e3cc8c76 · outbound

This paper cites Learning Sparse Neural Networks through $L_0$ Regularization.

prunAdag: an adaptive pruning-aware gradient method Learning Sparse Neural Networks through $L_0$ Regularization

Reference 24

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Observation 8314f3a6-b149-4c20-aa07-ac7dc0601976 · outbound

This paper cites Compression-aware trai ning of deep networks,.

prunAdag: an adaptive pruning-aware gradient method Compression-aware trai ning of deep networks,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:08.005762Z

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Observation 8314ac11-3659-4278-b4af-35807abfae8d · outbound

This paper cites Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures.

prunAdag: an adaptive pruning-aware gradient method Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures

Reference 26

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Observation e0ca11c7-bb4b-49fa-895c-d7070a365681 · outbound

This paper cites Sparsity in deep learning: Pruning and growth for eﬃcient inference and training in neural network s,.

prunAdag: an adaptive pruning-aware gradient method Sparsity in deep learning: Pruning and growth for eﬃcient inference and training in neural network s,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.995490Z

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Observation 790b78b8-486c-4b01-a573-446efe91552c · outbound

This paper cites Dynamic network surgery for eﬃcient DNNs,.

prunAdag: an adaptive pruning-aware gradient method Dynamic network surgery for eﬃcient DNNs,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.984412Z

Source-reported events for the cited work

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Observation 1c0e3fd9-9018-49da-a554-3d3ea0db14c1 · outbound

This paper cites Scalable training of artiﬁcial neural networks with adaptive sparse connectivi ty inspired by network science,.

prunAdag: an adaptive pruning-aware gradient method Scalable training of artiﬁcial neural networks with adaptive sparse connectivi ty inspired by network science,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.974413Z

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Observation 7935c2bd-7dba-4c92-9498-d0236e17db73 · outbound

This paper cites Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks.

prunAdag: an adaptive pruning-aware gradient method Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Reference 30

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Observation b50b5fd4-0f93-4cb2-a287-f79d13afdc67 · outbound

This paper cites Deep Neural Network Training with Frank-Wolfe.

prunAdag: an adaptive pruning-aware gradient method Deep Neural Network Training with Frank-Wolfe

Reference 31

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Observation f228a0b5-b228-4277-8c02-7ad9b0b53b5d · outbound

This paper cites Pruning Convolutional Neural Networks for Resource Efficient Inference.

prunAdag: an adaptive pruning-aware gradient method Pruning Convolutional Neural Networks for Resource Efficient Inference

Reference 32

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Observation e2b60522-f285-4510-8c79-ede53eed9567 · outbound

This paper cites Faster gaze prediction with dense networks and Fisher pruning.

prunAdag: an adaptive pruning-aware gradient method Faster gaze prediction with dense networks and Fisher pruning

Reference 33

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Observation f43b615d-559d-45cf-8949-68032ddf7580 · outbound

This paper cites Learn ing pruning-friendly networks via Frank-Wolfe: One-shot, any-sparsity, and no retraining,.

prunAdag: an adaptive pruning-aware gradient method Learn ing pruning-friendly networks via Frank-Wolfe: One-shot, any-sparsity, and no retraining,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.963568Z

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Observation 49337144-6908-45ff-a7b1-509455272d5a · outbound

This paper cites Sparse predicti on with the k-support norm,.

prunAdag: an adaptive pruning-aware gradient method Sparse predicti on with the k-support norm,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.953074Z

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Observation 236a8c90-a82d-4583-b51e-0427c83675b7 · outbound

This paper cites The group k-support norm for learning with structured sparsity,.

prunAdag: an adaptive pruning-aware gradient method The group k-support norm for learning with structured sparsity,

Reference 36

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Observation a7aa2ce3-dbbc-4efd-b470-ae959bbd8bbe · outbound

This paper cites An algorithm for quadratic programming,.

prunAdag: an adaptive pruning-aware gradient method An algorithm for quadratic programming,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.932402Z

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Observation 61233321-6eef-4ffd-8d80-8059dbec785a · outbound

This paper cites Constrained minimizati on methods,.

prunAdag: an adaptive pruning-aware gradient method Constrained minimizati on methods,

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.922943Z

Source-reported events for the cited work

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Observation 4f84543b-97f8-4375-82a6-db4a20e53f20 · outbound

This paper cites Stochasti c Frank-Wolfe methods for nonconvex optimiza- tion,.

prunAdag: an adaptive pruning-aware gradient method Stochasti c Frank-Wolfe methods for nonconvex optimiza- tion,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.912981Z

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Observation fce8ebf8-de76-4af6-adb5-88c91bea043c · outbound

This paper cites WNGrad: Learn the Learning Rate in Gradient Descent.

prunAdag: an adaptive pruning-aware gradient method WNGrad: Learn the Learning Rate in Gradient Descent

Reference 40

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This paper cites On the Lambert W function,.

prunAdag: an adaptive pruning-aware gradient method On the Lambert W function,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.902915Z

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Observation 17021cd3-7042-4fc6-85fa-60fdf00c4d7c · outbound

This paper cites Sparco: A testing framework for sparse reconstruction,.

prunAdag: an adaptive pruning-aware gradient method Sparco: A testing framework for sparse reconstruction,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.892258Z

Source-reported events for the cited work

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Observation f2ab038a-bff9-4808-a6be-432b4efa0f9d · outbound

This paper cites S2MPJ and CUTEst optimization problems for Matlab, Python and Julia.

prunAdag: an adaptive pruning-aware gradient method S2MPJ and CUTEst optimization problems for Matlab, Python and Julia

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-08T05:44:07.630411Z

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Observation a27cb739-6d3c-4083-8f94-72daf21a933c · outbound

This paper cites A fast algorit hm for sparse reconstruction based on shrinkage, subspace optimization, and continuation,.

prunAdag: an adaptive pruning-aware gradient method A fast algorit hm for sparse reconstruction based on shrinkage, subspace optimization, and continuation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.881806Z

Source-reported events for the cited work

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Observation 65bca56e-8161-4e2e-88aa-ae62ea6c3413 · outbound

This paper cites A variable ﬁxing version of the two-block nonlinear constrained Gauss-Seidel algorithm for ℓ 1-regularized least-squares,.

prunAdag: an adaptive pruning-aware gradient method A variable ﬁxing version of the two-block nonlinear constrained Gauss-Seidel algorithm for ℓ 1-regularized least-squares,

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.872375Z

Source-reported events for the cited work

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Observation 1513203e-f6be-43cb-a5e7-10cd04d69ea2 · outbound

This paper cites K-SVD: An algori thm for designing overcomplete dictionaries for sparse representation,.

prunAdag: an adaptive pruning-aware gradient method K-SVD: An algori thm for designing overcomplete dictionaries for sparse representation,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.863187Z

Source-reported events for the cited work

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Observation 4217440c-ea27-44d6-9ee5-194b43ee4ff2 · outbound

This paper cites UCI machine learning repository,.

prunAdag: an adaptive pruning-aware gradient method UCI machine learning repository,

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.852794Z

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 ce725356-6471-41a6-be3b-d69314cbb7cc · outbound

This paper cites LIBSVM: A library for suppor t vector machines,.

prunAdag: an adaptive pruning-aware gradient method LIBSVM: A library for suppor t vector machines,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T05:44:07.842732Z

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