Paper Citation Record · LEDGER

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks

As of 7 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2505.17962.

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

pith.paper-citation-record.v1
2505.17962 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:45:14.558674Z

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+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

34 of 34 outbound references displayed

verified exact6
verified fuzzy12
unresolved16
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 57b0dde0-d6a9-4d0f-87d0-8f791002e64f · outbound

This paper cites Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:11.260206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8db653db-f171-4797-8526-13b2c28573cf · outbound

This paper cites XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:11.332583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6a2e2cb-1f13-4d5f-8b9a-3c7bd86aee97 · outbound

This paper cites Deep learning in spiking neural networks.Neural Networks, 111: 47–63, March 2019.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Deep learning in spiking neural networks.Neural Networks, 111: 47–63, March 2019

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:11.336795Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 849ce33a-405e-4c7e-8696-4f18868de7a9 · outbound

This paper cites Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Surrogate gradient learning in spiking neural networks: Bringing the power of gradient-based optimization to spiking neural networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:11.360996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d0970c9-3f99-47d8-b456-f954fd7d30eb · outbound

This paper cites Understanding straight-through estimator in train- ing activation quantized neural nets.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Understanding straight-through estimator in train- ing activation quantized neural nets

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:18.664874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a95ea7b9-ce5e-484c-b01c-4246f12d7570 · outbound

This paper cites Weight uncertainty in neural networks.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Weight uncertainty in neural networks

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:18.474279Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7b5908aa-a81e-4138-a786-5630b2513384 · outbound

This paper cites Variational dropout and the local reparameterization trick.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Variational dropout and the local reparameterization trick

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:18.293999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 40d8abed-205c-4e78-9dc6-4a357ef023cb · outbound

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

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Learning multiple layers of features from tiny images

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:11.872147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9f8d31fb-a5d2-4d32-961f-af9ab4ddd7fb · outbound

This paper cites The heidelberg spiking datasets: A machine learning perspective on neuromorphic vision sensing.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks The heidelberg spiking datasets: A machine learning perspective on neuromorphic vision sensing

Reference 9

Resolution: verified exact; raw_fallback, observed 2026-08-07T14:45:15.998478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 80f2311f-6221-4f48-beff-e9f72a13e063 · outbound

This paper cites A low power, fully event-based gesture recognition system.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks A low power, fully event-based gesture recognition system

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:18.067640Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 8628d9e6-7ad0-43aa-b004-e16d41598710 · outbound

This paper cites Evaluating the variance of likelihood-ratio gradient estimators.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Evaluating the variance of likelihood-ratio gradient estimators

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:17.862050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 90fb04e6-d9d2-48ed-933c-a65183112b91 · outbound

This paper cites Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:12.290735Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17d0a09c-7534-4703-968d-329760850b51 · outbound

This paper cites Bias-variance tradeoffs in single-sample binary gradient estimators,.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Bias-variance tradeoffs in single-sample binary gradient estimators,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:17.671555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fa71bd69-c2cd-4599-9b58-7e3fe0ecffc6 · outbound

This paper cites Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Rao-Blackwellizing the Straight-Through Gumbel-Softmax Gradient Estimator

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:12.559983Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 328758c1-33da-481d-b0d0-51111f58b101 · outbound

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

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Elucidating the theoretical underpinnings of surrogate gradient learning in spiking neural networks

Reference 15

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:45:15.430933Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7965955a-9f3a-425c-bc31-887489408908 · outbound

This paper cites Probabilistic Binary Neural Networks.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Probabilistic Binary Neural Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:12.780705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3932a7e-ecf5-46e4-ae5c-3c2b8fc42990 · outbound

This paper cites Training binary neural networks using the bayesian learning rule, 2020.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Training binary neural networks using the bayesian learning rule, 2020

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:17.487521Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b91fd431-5c5f-4002-96a6-342db4266975 · outbound

This paper cites Kistler.Spiking Neuron Models: Single Neurons, Populations, Plasticity.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Kistler.Spiking Neuron Models: Single Neurons, Populations, Plasticity

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:12.973302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed20083c-0254-464f-9682-cd938201fa44 · outbound

This paper cites Long short-term memory.Neural computation, 9(8): 1735–1780, 1997.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Long short-term memory.Neural computation, 9(8): 1735–1780, 1997

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:13.085592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5676f897-66ab-429a-92a1-5b1b06de6940 · outbound

This paper cites Learning phrase representations using rnn encoder- decoder for statistical machine translation.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Learning phrase representations using rnn encoder- decoder for statistical machine translation

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:17.297527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 46410b43-1d4f-4183-a117-73f1b748953b · outbound

This paper cites Simple statistical gradient-following algorithms for connectionist reinforce- ment learning.Machine learning, 8(3):229–256, 1992.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Simple statistical gradient-following algorithms for connectionist reinforce- ment learning.Machine learning, 8(3):229–256, 1992

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:13.340723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42d6b746-97af-48f8-b72d-dca3a3d7723b · outbound

This paper cites ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks ARM: Augment-REINFORCE-Merge Gradient for Stochastic Binary Networks

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:45:15.250500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 045f699c-68f5-4f81-9b29-b8f028e9334e · outbound

This paper cites Automatic Differentiation of Programs with Discrete Randomness.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Automatic Differentiation of Programs with Discrete Randomness

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:45:15.023890Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 7e4b15d4-7591-4164-b2f4-0b5e8cd967b9 · outbound

This paper cites Categorical reparameterization with gumbel-softmax.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Categorical reparameterization with gumbel-softmax

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:17.114951Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 2fea1849-06b6-4990-bba0-cb7966a5221a · outbound

This paper cites Variational Dropout via Empirical Bayes.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Variational Dropout via Empirical Bayes

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:45:14.818433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 02bf7839-1a0b-4b63-a0c7-30476316067f · outbound

This paper cites Wunderlich and Christoph Pehle.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Wunderlich and Christoph Pehle

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:13.823782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ca6c9f68-273b-4a49-9fc0-adebc61f1988 · outbound

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

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:13.937697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c36e1d84-15c1-4be7-b46b-7e675927c121 · outbound

This paper cites Auto-encoding variational bayes.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Auto-encoding variational bayes

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T14:45:14.044368Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 80b61ad2-1fcb-46b3-bb44-b378d8ee8ed1 · outbound

This paper cites The concrete distribution: A continuous relaxation of discrete random variables.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks The concrete distribution: A continuous relaxation of discrete random variables

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:16.967174Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation beeae3ab-cffc-4857-a047-d8553dc3d0f1 · outbound

This paper cites each neuron’s probability of firing is a linear combination of its inputs.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks each neuron’s probability of firing is a linear combination of its inputs

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:16.787321Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation a67eb01c-b854-4e6b-b7f9-e46f014599b6 · outbound

This paper cites an unresolved cited work.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Unresolved cited work

Reference 32

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:45:16.567286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation c94ede06-4ab5-4cb7-8acb-22227fb32ddf · outbound

This paper cites an unresolved cited work.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Unresolved cited work

Reference 33

Resolution: unresolved; raw_fallback, observed 2026-08-07T14:45:16.355956Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation b412c48c-b486-46ee-85b4-7c817c088ee9 · outbound

This paper cites noiseless.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks noiseless

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:45:16.157614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 3d800b6d-0304-4000-96fa-bab764c135b0 · outbound

This paper cites Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators.

A Principled Bayesian Framework for Training Binary and Spiking Neural Networks Bias-Variance Tradeoffs in Single-Sample Binary Gradient Estimators

Reference 2021

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:45:15.715324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Pith citing papers

No inbound Pith citation observations are available.