Paper Citation Record · LEDGER

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce

As of 13 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2411.18250.

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

pith.paper-citation-record.v1
2411.18250 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T11:27:36.695961Z

measured 12 of 12 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 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

12 of 12 outbound references displayed

verified exact1
verified fuzzy2
unresolved9
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 20deaecb-fd4f-4cd2-8491-d7ebd20afde9 · outbound

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

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T11:27:36.652682Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b2a2651-f704-45ae-b7a5-749586180695 · outbound

This paper cites Spatio- temporal backpropagation for training high-performance spiking neural networks.

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce Spatio- temporal backpropagation for training high-performance spiking neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:27:36.826123Z

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 41af19ab-358c-4104-9f38-a4df677df71d · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-12T11:27:36.687742Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4d3b978-251e-4784-93f8-c7c93ad5b209 · outbound

This paper cites an unresolved cited work.

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-12T11:27:36.815230Z

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 51a428e5-562b-4ee8-8d3b-610d3d0bb3f6 · outbound

This paper cites M., Potempa, K., Versari, L., Fischbacher, T., Gesmundo, A., and Alakuijala, J.

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce M., Potempa, K., Versari, L., Fischbacher, T., Gesmundo, A., and Alakuijala, J

Reference 2000

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T11:27:36.844598Z

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 887e003b-0d83-4667-b171-85a57cd3f11d · outbound

This paper cites Training Deep Spiking Neural Networks.

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce Training Deep Spiking Neural Networks

Reference 2002

Resolution: verified exact; local_arxiv, observed 2026-08-12T11:27:36.764346Z

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 1f5802c3-f375-4067-ba11-51842c34fbea · outbound

This paper cites Identifying Generalization Properties in Neural Networks.

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce Identifying Generalization Properties in Neural Networks

Reference 2007

Resolution: unresolved; no resolver link, observed 2026-08-12T11:27:36.680227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62f76902-b57a-4bb9-9ca8-8f725989049d · outbound

This paper cites Elucidating the theoretical underpinnings of surrogate gradient learning in spiking neural networks.

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce Elucidating the theoretical underpinnings of surrogate gradient learning in spiking neural networks

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-12T11:27:36.660529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 29bd4e02-a350-4997-982d-bd2c2e862d6a · outbound

This paper cites Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift.

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-12T11:27:36.664498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ae6a9ce-456c-490d-ae12-109ad1e100cc · outbound

This paper cites Dropout: a simple way to prevent neural networks from overfitting.

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce Dropout: a simple way to prevent neural networks from overfitting

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-12T11:27:36.676514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9d3b9826-6193-4b14-9cc4-97358dccffe5 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce Adam: A Method for Stochastic Optimization

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-12T11:27:36.668295Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e78e497c-413a-4161-9c8b-805bd81be770 · outbound

This paper cites Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets.

IKUN: Initialization to Keep snn training and generalization great with sUrrogate-stable variaNce Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-12T11:27:36.695961Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.