Paper Citation Record · LEDGER

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's

As of 23 August 2026, this Paper Citation Record lists 18 of 18 outbound references and 0 inbound Pith citation observations for arXiv:2509.05446.

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

pith.paper-citation-record.v1
2509.05446 v1

Coverage vector

measured 18 of 18 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T05:27:46.215898Z

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+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

18 of 18 outbound references displayed

verified exact1
verified fuzzy16
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0aaa3ce1-6215-44d6-b16e-be255498bdea · outbound

This paper cites Deep residual learning for image recognition,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's Deep residual learning for image recognition,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.465540Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d873f71d-d0c8-42dc-9d09-6c9c946bb9fc · outbound

This paper cites Fully convolutional networks for semantic segmentation,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's Fully convolutional networks for semantic segmentation,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.454175Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7abf3936-9303-4125-ab4d-40594c4973c1 · outbound

This paper cites SSD: Single shot multibox detector,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's SSD: Single shot multibox detector,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.442346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b32c273a-b319-464a-99d5-d5b15ea95808 · outbound

This paper cites ImageNet classification with deep convolutional neural networks,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's ImageNet classification with deep convolutional neural networks,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.430911Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3bf6ba65-5572-4cb2-a39d-4a779a1970b6 · outbound

This paper cites Structured pruning for deep convolutional neural networks: A survey,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's Structured pruning for deep convolutional neural networks: A survey,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.419378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ba0aa4ea-6627-42ca-8cb1-080b961b668e · outbound

This paper cites Methods for pruning deep neural networks,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's Methods for pruning deep neural networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.406631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9c4008fe-6a84-4678-8bd5-9da5b8f1cc9f · outbound

This paper cites Monotonic value function factorisation for deep multi- agent reinforcement learning,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's Monotonic value function factorisation for deep multi- agent reinforcement learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.378154Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d3fa35c9-f6d0-4e5c-8245-518e4cb018b4 · outbound

This paper cites Pruning filters for efficient ConvNets,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's Pruning filters for efficient ConvNets,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.393193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e1f7df94-991f-4bf9-9e2b-8314ecd1817e · outbound

This paper cites Importance estimation for neural network pruning,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's Importance estimation for neural network pruning,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.364220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ca682bbb-5a4f-4275-af60-828e89ddb226 · outbound

This paper cites Variational dropout sparsifies deep neural networks,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's Variational dropout sparsifies deep neural networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.350368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1936086c-45b0-4bf6-8958-d312b3bf2de6 · outbound

This paper cites An efficient multi-agent reinforce- ment learning framework for neural network compression,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's An efficient multi-agent reinforce- ment learning framework for neural network compression,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.337956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 9b117ff2-df2f-4494-a903-377484dc8c3f · outbound

This paper cites A multi-agent reinforce- ment learning based approach for automatic filter pruning,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's A multi-agent reinforce- ment learning based approach for automatic filter pruning,

Reference 13

Resolution: verified exact; doi, observed 2026-08-05T05:27:46.251653Z

Source-reported events for the cited work

correction dated 2025-04-23. Source: crossref record 10.1038/s41598-025-98325-0->10.1038/s41598-024-82562-w:correction, observed 2026-07-11T03:04:26.846673+00:00. This notice travels one citation hop only.

Observation e9e3e7ec-d22e-49ba-9d38-419cb12ed446 · outbound

This paper cites QLP: Deep Q-learning for pruning deep neural networks,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's QLP: Deep Q-learning for pruning deep neural networks,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.326273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ecc1cbf5-c3ca-4c75-aa2d-c0b6f65020b6 · outbound

This paper cites Distilling the knowledge in a neural network,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's Distilling the knowledge in a neural network,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.313045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation f33f1401-513a-42fa-969a-53df7605fc1c · outbound

This paper cites Learning efficient convolutional networks through network slimming,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's Learning efficient convolutional networks through network slimming,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.299073Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 23bb4291-282d-419f-9dc3-0e5a0bbd848a · outbound

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

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's Learning multiple layers of features from tiny images,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.286267Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a07aa1bb-6727-4154-8315-8c5721ac6d1a · outbound

This paper cites Very deep convolutional networks for large-scale image recognition,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's Very deep convolutional networks for large-scale image recognition,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T05:27:46.212297Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6e4c937-a835-48dd-a610-4043178a5675 · outbound

This paper cites An image is worth 16×16 words: Transformers for image recognition at scale,.

Dynamic Sensitivity Filter Pruning using Multi-Agent Reinforcement Learning For DCNN's An image is worth 16×16 words: Transformers for image recognition at scale,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T05:27:46.264884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.