Paper Citation Record · LEDGER

Efficient ANN-SNN Conversion with Error Compensation Learning

As of 19 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 2 inbound Pith citation observations for arXiv:2506.01968.

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

pith.paper-citation-record.v1
2506.01968 v1

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:17:50.267940Z

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:03:58.547868Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T10:26:02.366417Z

Reference resolution

14 of 14 outbound references displayed

verified exact0
verified fuzzy6
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3f3c4296-d2b3-4c88-b844-293a6baa3004 · outbound

This paper cites (12) This paper uses the IF model for ANN-SNN conversion, which al lows neurons to accumulate potential through input current and emit spikes when the threshold is reached.

Efficient ANN-SNN Conversion with Error Compensation Learning (12) This paper uses the IF model for ANN-SNN conversion, which al lows neurons to accumulate potential through input current and emit spikes when the threshold is reached

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:50.640428Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7f656f27-7984-44b3-9636-50878ac87b5d · outbound

This paper cites Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks.

Efficient ANN-SNN Conversion with Error Compensation Learning Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:50.173528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1817423a-871a-41cb-9873-6ed858f76f5a · outbound

This paper cites Optical ﬂow-guided 6dof object pose tracking with an event camera.

Efficient ANN-SNN Conversion with Error Compensation Learning Optical ﬂow-guided 6dof object pose tracking with an event camera

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:50.890367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3c5a68e4-cf98-4ad9-b55e-53aea25a79a9 · outbound

This paper cites Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation.

Efficient ANN-SNN Conversion with Error Compensation Learning Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:50.200334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06fac346-1a8c-437a-b555-760f7dd97143 · outbound

This paper cites PRELIMINARIES A.1.

Efficient ANN-SNN Conversion with Error Compensation Learning PRELIMINARIES A.1

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:50.666617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 842597d8-c585-45fa-beb6-2aa9adc8bc12 · outbound

This paper cites It is worth noting that when T = 2 , our method achieved an accuracy of 72.58% for VGG16, which is 8.79% higher than th e suboptimal QCFS method.

Efficient ANN-SNN Conversion with Error Compensation Learning It is worth noting that when T = 2 , our method achieved an accuracy of 72.58% for VGG16, which is 8.79% higher than th e suboptimal QCFS method

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:50.604751Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9516c099-fdd8-4142-907c-dd0359186a8e · outbound

This paper cites an unresolved cited work.

Efficient ANN-SNN Conversion with Error Compensation Learning Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:17:50.573520Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 12d4b721-638c-4dfa-ad89-e7ee3e5d2735 · outbound

This paper cites an unresolved cited work.

Efficient ANN-SNN Conversion with Error Compensation Learning Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-15T22:17:50.544417Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ef7fc442-0e0f-4250-a7c3-490043f2ed46 · outbound

This paper cites Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks.

Efficient ANN-SNN Conversion with Error Compensation Learning Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:50.154476Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e558673-143a-4c65-a441-7e68f7a317b8 · outbound

This paper cites Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks.

Efficient ANN-SNN Conversion with Error Compensation Learning Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:50.207232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2f8fb2a-a045-45f8-b745-134c6b1faab2 · outbound

This paper cites U., Neil, D., Binas, J., Cook, M., Liu, S.-C., and Pfeiffer, M.

Efficient ANN-SNN Conversion with Error Compensation Learning U., Neil, D., Binas, J., Cook, M., Liu, S.-C., and Pfeiffer, M

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:50.943446Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e54bd606-94ab-4f2a-8388-154a9602a640 · outbound

This paper cites Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks.

Efficient ANN-SNN Conversion with Error Compensation Learning Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networks

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:50.146523Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c783760b-b672-4546-a090-4bf02463f393 · outbound

This paper cites and Chang, I.-J.

Efficient ANN-SNN Conversion with Error Compensation Learning and Chang, I.-J

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:17:50.915458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fc5e86e4-d07c-4556-876c-e5ce5cb36a0e · outbound

This paper cites Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency.

Efficient ANN-SNN Conversion with Error Compensation Learning Improving the Sparse Structure Learning of Spiking Neural Networks from the View of Compression Efficiency

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T22:17:50.214589Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation b83da171-f460-44b4-96c4-bf0fc73f78a5 · inbound

Spike-driven Large Language Model cites this paper.

Spike-driven Large Language Model Efficient ANN-SNN Conversion with Error Compensation Learning

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:26:02.369855Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 39689d8e-d290-4099-ad5c-7479b7bec823 · inbound

AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning cites this paper.

AS-FedBridge: Pseudo-Spike Bridge Distillation for Heterogeneous ANN-SNN Federated Learning Efficient ANN-SNN Conversion with Error Compensation Learning

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-05T21:03:58.547868Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.