Paper Citation Record · LEDGER

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits

As of 18 August 2026, this Paper Citation Record lists 19 of 19 outbound references and 0 inbound Pith citation observations for arXiv:2607.22564.

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

pith.paper-citation-record.v1
2607.22564 v1

Coverage vector

measured 19 of 19 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T12:45:40.643848Z

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+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

19 of 19 outbound references displayed

verified exact0
verified fuzzy0
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1df6983b-55d1-43d2-941e-3db50ea47909 · outbound

This paper cites Pruning Neural Networks Using Multi-Armed Bandits,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Pruning Neural Networks Using Multi-Armed Bandits,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:39.671588Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8ebf6e4c-0f9b-474d-b5f2-04805ff91820 · outbound

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

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Pruning Convolutional Neural Networks for Resource Efficient Inference,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:39.719877Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25bf11ff-17a6-4c9f-8f4c-a9586172d147 · outbound

This paper cites Optimal Brain Damage,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Optimal Brain Damage,

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:39.770097Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d82c6d2c-8cc6-43f7-8e55-50c65e0adb59 · outbound

This paper cites Second Order Derivatives for Network Pruning: Optimal Brain Surgeon,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Second Order Derivatives for Network Pruning: Optimal Brain Surgeon,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:39.814925Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc757caa-de25-4b7e-b6e8-21af1375d010 · outbound

This paper cites Finite-time Analysis of the Multiarmed Bandit Problem,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Finite-time Analysis of the Multiarmed Bandit Problem,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:39.870263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6264a55e-50cf-434c-b1ff-712746835e02 · outbound

This paper cites On the Likelihood that One Unknown Probability Exceeds Another in View of the Evidence of Two Samples,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits On the Likelihood that One Unknown Probability Exceeds Another in View of the Evidence of Two Samples,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:39.925108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 623c8859-ab72-4f5e-ab3a-8c319f80a8b3 · outbound

This paper cites Statistical Comparisons of Classifiers over Multiple Data Sets,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Statistical Comparisons of Classifiers over Multiple Data Sets,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:39.994406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff9cd1d5-932f-4366-9c4d-6e9ea763fb97 · outbound

This paper cites A Comparison of Alternative Tests of Significance for the Problem of m Rankings,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits A Comparison of Alternative Tests of Significance for the Problem of m Rankings,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:40.039198Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 427cacb8-a81c-42a6-a37c-2bc0733ff1a3 · outbound

This paper cites an unresolved cited work.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Unresolved cited work

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:40.099835Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86a5bf5f-4c82-4843-906f-d491bdb3a07e · outbound

This paper cites Pruning Filters for Efficient ConvNets,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Pruning Filters for Efficient ConvNets,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:40.154179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc39f68a-9bcf-4458-8ea6-dd80b623e396 · outbound

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

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Network Trimming: A Data-Driven Neuron Pruning Approach towards Efficient Deep Architectures

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:40.202171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d55d6766-499f-4e02-856d-12327b5d2933 · outbound

This paper cites Channel-Level Acceleration of Deep Face Representations,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Channel-Level Acceleration of Deep Face Representations,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:40.248974Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dd99eeb8-cdb8-4c4f-ba84-eb05319e54c1 · outbound

This paper cites Gradient-Based Learning Applied to Document Recognition,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Gradient-Based Learning Applied to Document Recognition,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:40.294414Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation afdd933f-548a-431b-9e2e-7affac4ceea0 · outbound

This paper cites Krizhevsky, I.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Krizhevsky, I

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:40.365532Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f53a4b5e-dd8f-4059-b871-db4e61d3d068 · outbound

This paper cites Learning Multiple Layers of Features from Tiny Images,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Learning Multiple Layers of Features from Tiny Images,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:40.406699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa3cbaa1-2772-4207-8b9c-06f22fa7916f · outbound

This paper cites Reading Digits in Natural Images with Unsupervised Feature Learning,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Reading Digits in Natural Images with Unsupervised Feature Learning,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:40.460963Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce8ca7eb-baf4-4463-8628-3fcb05534837 · outbound

This paper cites The Caltech-UCSD Birds-200- 2011 Dataset,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits The Caltech-UCSD Birds-200- 2011 Dataset,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:40.539688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1713c20a-f231-4f2a-adff-f4449a5be6bf · outbound

This paper cites Automated Flower Classification over a Large Number of Classes,.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Automated Flower Classification over a Large Number of Classes,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:40.602406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66bcbf5e-810e-4984-a613-95b13e9cb942 · outbound

This paper cites an unresolved cited work.

Loss-Aware Feature-Map Pruning in Convolutional Neural Networks Using Multi-Armed Bandits Unresolved cited work

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T12:45:40.643848Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.