Paper Citation Record · LEDGER

QP-SNN: Quantized and Pruned Spiking Neural Networks

As of 8 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 3 inbound Pith citation observations for arXiv:2502.05905.

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

pith.paper-citation-record.v1
2502.05905 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T17:34:36.984477Z

measured 35 of 35 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T06:57:39.098683Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T10:46:17.279572Z

Reference resolution

32 of 32 outbound references displayed

verified exact3
verified fuzzy12
unresolved17
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

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Outbound references

Observation 56749f71-31a2-457d-848b-ecd991cc44a8 · outbound

This paper cites Binary event-driven spiking transformer.arXiv preprint arXiv:2501.05904,.

QP-SNN: Quantized and Pruned Spiking Neural Networks Binary event-driven spiking transformer.arXiv preprint arXiv:2501.05904,

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-08-08T17:34:37.779000Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation cd6b438e-09ac-4275-b949-fb431d2c5b3c · outbound

This paper cites Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting.

QP-SNN: Quantized and Pruned Spiking Neural Networks Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.853584Z

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Observation 804cab74-3c54-4759-bb64-45404e226620 · outbound

This paper cites In contrast, theavalue for the subsequent layers are predominantly around 0.2, resulting in a significantly lower bit-width utilization rate of approximately 20.31%.

QP-SNN: Quantized and Pruned Spiking Neural Networks In contrast, theavalue for the subsequent layers are predominantly around 0.2, resulting in a significantly lower bit-width utilization rate of approximately 20.31%

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:37.898120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 457266fe-8d41-4f21-8b21-6fa895c06936 · outbound

This paper cites Towards accurate post-training quantization for vision transformer.

QP-SNN: Quantized and Pruned Spiking Neural Networks Towards accurate post-training quantization for vision transformer

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.050812Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8ea7e88f-daa9-4087-9bbd-534ba33aeab5 · outbound

This paper cites Moreover, theavalue of subsequent layers is mainly around 0.3, resulting in a significantly lower bit width utilization rate of about 30.27%.

QP-SNN: Quantized and Pruned Spiking Neural Networks Moreover, theavalue of subsequent layers is mainly around 0.3, resulting in a significantly lower bit width utilization rate of about 30.27%

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:37.882128Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f2ff60bd-7a34-445d-915e-9cb00b14b042 · outbound

This paper cites Spike-thrift: Towards energy- efficient deep spiking neural networks by limiting spiking activity via attention-guided compres- sion.

QP-SNN: Quantized and Pruned Spiking Neural Networks Spike-thrift: Towards energy- efficient deep spiking neural networks by limiting spiking activity via attention-guided compres- sion

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.034457Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4aad932b-08b6-491a-a082-ba733016ef28 · outbound

This paper cites Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning.

QP-SNN: Quantized and Pruned Spiking Neural Networks Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.884254Z

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Observation bfac4511-b73c-48f4-9a20-aab1559c8144 · outbound

This paper cites Furthermore, we calculatedAvgCosS l for each layer in ResNet20, and themin l AvgCosSl is 0.870.

QP-SNN: Quantized and Pruned Spiking Neural Networks Furthermore, we calculatedAvgCosS l for each layer in ResNet20, and themin l AvgCosSl is 0.870

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:37.865321Z

Source-reported events for the cited work

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Observation a81271fe-bdcf-4d25-b283-4f7e93a3ae6f · outbound

This paper cites LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization.

QP-SNN: Quantized and Pruned Spiking Neural Networks LitE-SNN: Designing Lightweight and Efficient Spiking Neural Network through Spatial-Temporal Compressive Network Search and Joint Optimization

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.893724Z

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Observation 40ab340f-7d39-440f-a2b9-bd1fe6d7a054 · outbound

This paper cites Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection.

QP-SNN: Quantized and Pruned Spiking Neural Networks Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detection

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.898597Z

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Observation 807a0de7-987c-4954-901f-dfba8c72e61c · outbound

This paper cites Spinnaker: A 1-w 18-core system- on-chip for massively-parallel neural network simulation.IEEE Journal of Solid-State Circuits, 48(8):1943–1953,.

QP-SNN: Quantized and Pruned Spiking Neural Networks Spinnaker: A 1-w 18-core system- on-chip for massively-parallel neural network simulation.IEEE Journal of Solid-State Circuits, 48(8):1943–1953,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.000584Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b488fde3-2b7b-4e8a-a59b-1c470b3b83b6 · outbound

This paper cites an unresolved cited work.

QP-SNN: Quantized and Pruned Spiking Neural Networks Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-08T17:34:37.830842Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 110dc959-13be-4f5d-84e1-ffe47e9f30c7 · outbound

This paper cites Ternary Spike-based Neuromorphic Signal Processing System.

QP-SNN: Quantized and Pruned Spiking Neural Networks Ternary Spike-based Neuromorphic Signal Processing System

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-08T17:34:37.390564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1fbbcc8a-3720-4502-b0e9-e17ee44b7402 · outbound

This paper cites Q-SNNs: Quantized Spiking Neural Networks.

QP-SNN: Quantized and Pruned Spiking Neural Networks Q-SNNs: Quantized Spiking Neural Networks

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-08T17:34:37.366801Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8b077f3d-7c72-4be0-b37b-17a2e697b43b · outbound

This paper cites Convolutional neural network pruning: A survey.

QP-SNN: Quantized and Pruned Spiking Neural Networks Convolutional neural network pruning: A survey

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:37.968166Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 13f19520-d51c-46ad-9e08-0bade6377bd4 · outbound

This paper cites Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips.

QP-SNN: Quantized and Pruned Spiking Neural Networks Spike-driven Transformer V2: Meta Spiking Neural Network Architecture Inspiring the Design of Next-generation Neuromorphic Chips

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.936520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d836743d-cc76-476a-bd24-fa5df137d094 · outbound

This paper cites Spikingformer: Spike-driven residual learning for transformer-based spiking neural net- work.arXiv preprint arXiv:2304.11954,.

QP-SNN: Quantized and Pruned Spiking Neural Networks Spikingformer: Spike-driven residual learning for transformer-based spiking neural net- work.arXiv preprint arXiv:2304.11954,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.946005Z

Source-reported events for the cited work

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Observation fa4861b7-e63f-4dea-90d3-da497093536b · outbound

This paper cites Trained Ternary Quantization.

QP-SNN: Quantized and Pruned Spiking Neural Networks Trained Ternary Quantization

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.950376Z

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Observation 09e7506a-909e-4f7a-a638-579bcc439648 · outbound

This paper cites Moreover, it is worth noting that the advanced works (Deng et al.

QP-SNN: Quantized and Pruned Spiking Neural Networks Moreover, it is worth noting that the advanced works (Deng et al

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:37.916405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b33f7a12-8527-405c-b01c-63cc58f59d3a · outbound

This paper cites We also calculateAvgCosS l for each layer in VGGSNN, and themin l AvgCosSl is 0.952.

QP-SNN: Quantized and Pruned Spiking Neural Networks We also calculateAvgCosS l for each layer in VGGSNN, and themin l AvgCosSl is 0.952

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:37.847943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 852d2e2c-68ef-44b9-8f94-5da9d016f24b · outbound

This paper cites Pruning Filters for Efficient ConvNets.

QP-SNN: Quantized and Pruned Spiking Neural Networks Pruning Filters for Efficient ConvNets

Reference 2002

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.879781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4427b678-abf1-4b92-9a2e-f7819cb2f4bf · outbound

This paper cites Deep Rewiring: Training very sparse deep networks.

QP-SNN: Quantized and Pruned Spiking Neural Networks Deep Rewiring: Training very sparse deep networks

Reference 2005

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.831819Z

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Observation 2e88baa5-6ec0-4058-8e6d-41c0323d9f95 · outbound

This paper cites an unresolved cited work.

QP-SNN: Quantized and Pruned Spiking Neural Networks Unresolved cited work

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.869707Z

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Unavailable: canonical work link unavailable.

Observation 679d6d7b-31dd-4e0d-8a76-7ea6b03f1db6 · outbound

This paper cites First, the initial SNN model undergoes ReScaW-based uniform quantization, where weights are rescaled and quantized, followed by iterative training with backpropagation.

QP-SNN: Quantized and Pruned Spiking Neural Networks First, the initial SNN model undergoes ReScaW-based uniform quantization, where weights are rescaled and quantized, followed by iterative training with backpropagation

Reference 2016

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:37.933866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation dd812220-1c1e-4d28-b2c3-9ae958f46558 · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

QP-SNN: Quantized and Pruned Spiking Neural Networks Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.837460Z

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Observation 54abe36c-44df-41e0-a4cb-7a1de54517d0 · outbound

This paper cites A comprehensive survey on model quantization for deep neural networks in image classification.ACM Transactions on Intelligent Systems and Technology, 14(6):1–50,.

QP-SNN: Quantized and Pruned Spiking Neural Networks A comprehensive survey on model quantization for deep neural networks in image classification.ACM Transactions on Intelligent Systems and Technology, 14(6):1–50,

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:37.984609Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation aeed5f5d-28d3-4569-bcac-252325ee0f3c · outbound

This paper cites an unresolved cited work.

QP-SNN: Quantized and Pruned Spiking Neural Networks Unresolved cited work

Reference 2019

Resolution: unresolved; raw_fallback, observed 2026-08-08T17:34:37.950943Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 05515f23-7c1d-49f6-b726-c1eb4010f0cd · outbound

This paper cites BiBERT: Accurate Fully Binarized BERT.

QP-SNN: Quantized and Pruned Spiking Neural Networks BiBERT: Accurate Fully Binarized BERT

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.907835Z

Source-reported events for the cited work

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Observation 94ea723c-cfe2-49fd-9641-9eb1b7559afd · outbound

This paper cites AutoAugment: Learning Augmentation Policies from Data.

QP-SNN: Quantized and Pruned Spiking Neural Networks AutoAugment: Learning Augmentation Policies from Data

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.848338Z

Source-reported events for the cited work

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Observation 5aa16484-6798-4d7f-88c4-62133cc09c75 · outbound

This paper cites Improved Regularization of Convolutional Neural Networks with Cutout.

QP-SNN: Quantized and Pruned Spiking Neural Networks Improved Regularization of Convolutional Neural Networks with Cutout

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.859012Z

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Observation 6e6c434b-cde7-48de-83f1-9bd9f4a5d08d · outbound

This paper cites SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges.

QP-SNN: Quantized and Pruned Spiking Neural Networks SVD Based Image Processing Applications: State of The Art, Contributions and Research Challenges

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-08T17:34:36.917642Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71d52958-6db0-4187-8fd6-2c453e3169e4 · outbound

This paper cites Neuromor- phic data augmentation for training spiking neural networks.

QP-SNN: Quantized and Pruned Spiking Neural Networks Neuromor- phic data augmentation for training spiking neural networks

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T17:34:38.017992Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Pith citing papers

Observation de04f556-a5c7-456d-b9b5-e8f92d1aeecf · inbound

SAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Network cites this paper.

SAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Network QP-SNN: Quantized and Pruned Spiking Neural Networks

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-18T10:46:17.282529Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation c83826cf-5478-4ff8-9815-a46bc32b0ac7 · inbound

Quantization of Spiking Neural Networks Beyond Accuracy cites this paper.

Quantization of Spiking Neural Networks Beyond Accuracy QP-SNN: Quantized and Pruned Spiking Neural Networks

Reference 14

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:50:25.041444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f4e8312d-7359-43ed-9f54-7e1d5e7c77e2 · inbound

Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks cites this paper.

Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks QP-SNN: Quantized and Pruned Spiking Neural Networks

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-02T06:57:39.098683Z

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Unavailable: canonical work link unavailable.