Paper Citation Record · LEDGER

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning

As of 12 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 1 inbound Pith citation observation for arXiv:2501.01118.

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

pith.paper-citation-record.v1
2501.01118 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:38:01.691375Z

measured 47 of 47 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-15T05:18:49.394115Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-15T05:19:46.005714Z

Reference resolution

46 of 46 outbound references displayed

verified exact2
verified fuzzy17
unresolved27
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 09bfcbe7-069d-4d75-9cdb-74662e132d5b · outbound

This paper cites Faster r-cnn: Towards real-time object detection with region proposal networks.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Faster r-cnn: Towards real-time object detection with region proposal networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.546535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 37867d4b-b343-4ca0-9205-0364a931aa49 · outbound

This paper cites Fully convolutional networks for se- mantic segmentation.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Fully convolutional networks for se- mantic segmentation

Reference 2

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Observation 08edfd5c-65f5-41fd-bf45-9e8fe2a4a40d · outbound

This paper cites Deep residual learning for image recognition.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Deep residual learning for image recognition

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.460748Z

Source-reported events for the cited work

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Observation c32f31b1-5d59-4349-800f-dcbba1769bd2 · outbound

This paper cites Revisiting unreasonable effectiveness of data in deep learning era.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Revisiting unreasonable effectiveness of data in deep learning era

Reference 4

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Observation 890b2636-996d-4f47-ac6b-139aa816c94d · outbound

This paper cites Deep bayesian active learning with image data.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Deep bayesian active learning with image data

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.471730Z

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Observation 111d1c11-a517-4a1f-9e12-58f5ce90f244 · outbound

This paper cites Active Learning for Convolutional Neural Networks: A Core-Set Approach.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Active Learning for Convolutional Neural Networks: A Core-Set Approach

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.476836Z

Source-reported events for the cited work

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Observation 2a9eded8-faad-43c4-9909-6ac26ff70a47 · outbound

This paper cites Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.483201Z

Source-reported events for the cited work

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Observation a283a4ec-697e-491d-a31d-31722760a47f · outbound

This paper cites Deep active learning for named entity recognition.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Deep active learning for named entity recognition

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.472742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation eadef394-0091-4f0b-bcca-ce93e23907c5 · outbound

This paper cites Active learning for convolutional neural networks: A core-set approach.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Active learning for convolutional neural networks: A core-set approach

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.455523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a7d2d9a5-f316-43a0-b74d-bca931d9a254 · outbound

This paper cites Selecting influential examples: Active learning with expected model output changes.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Selecting influential examples: Active learning with expected model output changes

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.434826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 080111f7-0b1a-480b-80be-528e7e3259c6 · outbound

This paper cites Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Active and Continuous Exploration with Deep Neural Networks and Expected Model Output Changes

Reference 11

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Observation 98f51567-7bb7-420f-8b78-14aead10ad13 · outbound

This paper cites Grad-match: Gradient matching based data subset selection for efficient deep model training.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Grad-match: Gradient matching based data subset selection for efficient deep model training

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.414211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9f2415d9-7f60-4bc1-ac05-4d34ae59b0ac · outbound

This paper cites Glister: A generalization based data selection framework for efficient and robust learning.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Glister: A generalization based data selection framework for efficient and robust learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.395241Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3826493e-0b7d-4412-8779-7c1209b207b7 · outbound

This paper cites Efficient data subset selection to generalize training across models: Transductive and inductive networks.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Efficient data subset selection to generalize training across models: Transductive and inductive networks

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.375968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 747b8f7e-6aac-4acf-8306-49070cd461bb · outbound

This paper cites Selection via Proxy: Efficient Data Selection for Deep Learning.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Selection via Proxy: Efficient Data Selection for Deep Learning

Reference 15

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Source-reported events for the cited work

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Observation 49540bc7-6a97-4675-b500-5cb47dc34b42 · outbound

This paper cites Neural Architecture Search with Reinforcement Learning.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Neural Architecture Search with Reinforcement Learning

Reference 16

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Observation fd4e58a4-e91f-4813-a7d3-939db9a7a344 · outbound

This paper cites Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Fbnetv2: Differentiable neural architecture search for spatial and channel dimensions

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.361166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 97ac6fd3-f716-4a58-8cfe-f44d2c652c14 · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 18

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Source-reported events for the cited work

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Observation 4132e8bc-ba78-483d-b1eb-04adc4e832bc · outbound

This paper cites Hawq-v2: Hessian aware trace-weighted quantization of neural networks.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Hawq-v2: Hessian aware trace-weighted quantization of neural networks

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.346266Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0a6ae1dd-712a-43fc-8bfb-e5b136d96276 · outbound

This paper cites Quantization and training of neural networks for efficient integer-arithmetic-only inference.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Quantization and training of neural networks for efficient integer-arithmetic-only inference

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.331630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 03587069-e773-44df-8b0d-bd9fd5e04a56 · outbound

This paper cites DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

Reference 21

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Source-reported events for the cited work

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Observation 06db42cc-bc79-4188-9514-c687e0a8ea67 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Distilling the Knowledge in a Neural Network

Reference 22

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Source-reported events for the cited work

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Observation 576fe4ca-b2b9-48a5-b487-012679b95491 · outbound

This paper cites Dreaming to distill: Data-free knowledge transfer via deepinversion.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Dreaming to distill: Data-free knowledge transfer via deepinversion

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.316786Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d02db78c-c7b9-462e-aa87-59501913dabb · outbound

This paper cites Faster CNNs with Direct Sparse Convolutions and Guided Pruning.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Faster CNNs with Direct Sparse Convolutions and Guided Pruning

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-08-10T22:38:02.026262Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9668c66e-4fe6-4223-82d0-cffe92e6893d · outbound

This paper cites Learning to prune deep neural networks via layer-wise optimal brain surgeon.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Learning to prune deep neural networks via layer-wise optimal brain surgeon

Reference 25

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Source-reported events for the cited work

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Observation 8f28d262-17b1-4a73-a6f3-263917994579 · outbound

This paper cites Dynamic network surgery for efficient dnns.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Dynamic network surgery for efficient dnns

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.588554Z

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Observation 6f5c539e-5852-4314-a5c3-aea85b9ff15b · outbound

This paper cites Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.594081Z

Source-reported events for the cited work

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Observation fd7325de-c1b4-46a6-84cf-8c5000f7a2ac · outbound

This paper cites Pruning Filters for Efficient ConvNets.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Pruning Filters for Efficient ConvNets

Reference 28

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Source-reported events for the cited work

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Observation 6a4ff8b7-f286-476c-a54c-c1bb8b3fe262 · outbound

This paper cites Channel pruning for accelerating very deep neural networks.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Channel pruning for accelerating very deep neural networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.281112Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e489483a-934a-4501-9e52-ede2ce48bcf5 · outbound

This paper cites Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.265433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation fc40670e-b30d-4811-b84a-63121dc06b9f · outbound

This paper cites Centripetal sgd for pruning very deep convolutional networks with complicated structure.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Centripetal sgd for pruning very deep convolutional networks with complicated structure

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.249815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5d06859d-ca72-49b6-99a6-6f4b37e45a35 · outbound

This paper cites Learning efficient convolutional networks through network slimming.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Learning efficient convolutional networks through network slimming

Reference 32

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Observation 97030c2e-4229-451d-9394-e3720a64ca98 · outbound

This paper cites Comparing Rewinding and Fine-tuning in Neural Network Pruning.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Comparing Rewinding and Fine-tuning in Neural Network Pruning

Reference 33

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Source-reported events for the cited work

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Observation 432adbc8-d059-4d1c-93a6-8ba7a2df2db0 · outbound

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

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning To prune, or not to prune: exploring the efficacy of pruning for model compression

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.633812Z

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Observation 547f3d11-6f6f-4aba-9a98-949f18c5abdb · outbound

This paper cites The State of Sparsity in Deep Neural Networks.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning The State of Sparsity in Deep Neural Networks

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.638883Z

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Unavailable: canonical work link unavailable.

Observation 60cf8a11-a33f-4456-a0ac-be3104eb6d90 · outbound

This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.644352Z

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Observation 9643f049-9b51-4dde-94c9-4edb18414b92 · outbound

This paper cites Pruning Neural Networks at Initialization: Why are We Missing the Mark?.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Pruning Neural Networks at Initialization: Why are We Missing the Mark?

Reference 37

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Observation d61a064b-ee90-46f8-a6d2-8ea561482eb0 · outbound

This paper cites Pruning neural networks without any data by iteratively conserving synaptic flow.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Pruning neural networks without any data by iteratively conserving synaptic flow

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.224222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3a026004-c7b7-4268-a0b2-885219486d8b · outbound

This paper cites Pruning from scratch.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Pruning from scratch

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.207963Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d6e61c45-6249-4a18-95f7-59cb8fe9d5cf · outbound

This paper cites Single Shot Structured Pruning Before Training.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Single Shot Structured Pruning Before Training

Reference 40

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Source-reported events for the cited work

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Observation b9820edd-256d-43de-ae9d-10d2e06dd093 · outbound

This paper cites Active learning.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Active learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T22:38:02.190631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 04742a54-4c80-43ff-9d45-f600a0cab9dd · outbound

This paper cites A mathematical theory of communication.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning A mathematical theory of communication

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.674415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61df00c1-545f-4dde-a555-5632b11e126e · outbound

This paper cites Learning multiple layers of features from tiny images.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Learning multiple layers of features from tiny images

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.679852Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5dac13b-0b5d-43a6-9903-c0afad153227 · outbound

This paper cites Tiny imagenet visual recognition challenge.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Tiny imagenet visual recognition challenge

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.685550Z

Source-reported events for the cited work

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Observation 05ecca45-15dc-4005-9919-29690e9a1901 · outbound

This paper cites Depgraph: Towards any structural pruning.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning Depgraph: Towards any structural pruning

Reference 45

Resolution: verified exact; raw_fallback, observed 2026-08-10T22:38:01.857866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation c58ea6d7-cb99-4270-94ba-7d07807ae073 · outbound

This paper cites doi: 10.18653/v1/W17-2630.

Pruning-based Data Selection and Network Fusion for Efficient Deep Learning doi: 10.18653/v1/W17-2630

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-10T22:38:01.494346Z

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Unavailable: canonical work link unavailable.

Pith citing papers

Observation 7e41a08c-3f39-4fad-8f18-afb6a0036ec5 · inbound

Are Candidate Models Really Needed for Active Learning? cites this paper.

Are Candidate Models Really Needed for Active Learning? Pruning-based Data Selection and Network Fusion for Efficient Deep Learning

Reference 179

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:19:46.009183Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.