Paper Citation Record · LEDGER

Training neural networks without backpropagation using particles

As of 12 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2412.05667.

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

pith.paper-citation-record.v1
2412.05667 v3

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T20:33:40.049829Z

measured 43 of 43 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

43 of 43 outbound references displayed

verified exact6
verified fuzzy14
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch4

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation da7c5173-de7a-4b12-914d-1e649b2b63c9 · outbound

This paper cites Gradient Followi ng Without Back-Propagation in Layered Networks.

Training neural networks without backpropagation using particles Gradient Followi ng Without Back-Propagation in Layered Networks

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:42.109046Z

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 222146c7-4163-45cb-a9b5-93dbf14d86e8 · outbound

This paper cites Gradients without Backpropagation.

Training neural networks without backpropagation using particles Gradients without Backpropagation

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.180370Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d2187e80-650a-4571-9e17-fe713e509a85 · outbound

This paper cites an unresolved cited work.

Training neural networks without backpropagation using particles Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:33:42.072725Z

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 e4c03353-6542-46c0-8180-7fd8708c4df0 · outbound

This paper cites an unresolved cited work.

Training neural networks without backpropagation using particles Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:33:42.050835Z

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 f682db04-1af0-4234-8f5f-8c7304f2fa0d · outbound

This paper cites How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective.

Training neural networks without backpropagation using particles How to Train Your Wide Neural Network Without Backprop: An Input-Weight Alignment Perspective

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:33:41.601298Z

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 0aceead8-f51b-4927-9017-fa1013c3a73c · outbound

This paper cites Supervised Learning in Ne ural Networks without Feed- back Networks.

Training neural networks without backpropagation using particles Supervised Learning in Ne ural Networks without Feed- back Networks

Reference 6

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T20:33:41.560386Z

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 c98f646b-9ea6-44c2-96bc-cb1da28d330d · outbound

This paper cites Classiﬁcation of Rice V ari eties Using Artiﬁcial Intelligence Methods.

Training neural networks without backpropagation using particles Classiﬁcation of Rice V ari eties Using Artiﬁcial Intelligence Methods

Reference 7

Resolution: verified exact; doi, observed 2026-08-11T20:33:40.284485Z

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 c1224e8a-ab4b-485a-8d5f-9910dca1ab4f · outbound

This paper cites Adaptive Subg radient Methods for Online Learning and Stochastic Optimization.

Training neural networks without backpropagation using particles Adaptive Subg radient Methods for Online Learning and Stochastic Optimization

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:42.023263Z

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 a4f6f2c1-68db-482b-b269-be4eb3439fc8 · outbound

This paper cites an unresolved cited work.

Training neural networks without backpropagation using particles Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-11T20:33:41.995133Z

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 1c311bd5-b9c4-4e8a-b6a7-2f3513d34310 · outbound

This paper cites Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges.

Training neural networks without backpropagation using particles Neuroevolution in Deep Neural Networks: Current Trends and Future Challenges

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:41.962488Z

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 5d785d45-6737-4dfc-aaa0-ee389d604dd6 · outbound

This paper cites Exponential natural evolut ion strategies.

Training neural networks without backpropagation using particles Exponential natural evolut ion strategies

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.247781Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c78f8306-d6bd-44d1-842a-c6890371ce86 · outbound

This paper cites An introduction to neural networks.

Training neural networks without backpropagation using particles An introduction to neural networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:41.940926Z

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 17638eb6-a167-4233-8af7-ccb51b46b1d8 · outbound

This paper cites Never look back - A modified EnKF method and its application to the training of neural networks without back propagation.

Training neural networks without backpropagation using particles Never look back - A modified EnKF method and its application to the training of neural networks without back propagation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.260281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bb82b38-9135-4660-b24f-91ba44400666 · outbound

This paper cites Non-Linear Back-propagation: Doing Back-Propagation without Deriva- tives of the Activation Function.

Training neural networks without backpropagation using particles Non-Linear Back-propagation: Doing Back-Propagation without Deriva- tives of the Activation Function

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:41.926845Z

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 50a63220-7de9-49f3-a263-9d9f7ab74418 · outbound

This paper cites The Forward-Forward Algorithm: Some Preliminary Investigations.

Training neural networks without backpropagation using particles The Forward-Forward Algorithm: Some Preliminary Investigations

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.464147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bda824a2-b18a-4858-b421-9820583b30fa · outbound

This paper cites Decoupled Neural Interfaces using Synthetic Gradients.

Training neural networks without backpropagation using particles Decoupled Neural Interfaces using Synthetic Gradients

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.509557Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9756aa5b-eeb5-436b-b879-594407a60536 · outbound

This paper cites One Forward is Enough for Neural Network Training via Likelihood Ratio Method.

Training neural networks without backpropagation using particles One Forward is Enough for Neural Network Training via Likelihood Ratio Method

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.515054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b115bdc9-12e2-4bac-9551-3d057b31822b · outbound

This paper cites Particle swarm optimization.

Training neural networks without backpropagation using particles Particle swarm optimization

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:41.910347Z

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 75c6dace-da87-4f61-add3-571ee1d2db13 · outbound

This paper cites Eberhart, and Shi Y.

Training neural networks without backpropagation using particles Eberhart, and Shi Y

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:41.889801Z

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 78cc137b-ea91-48b8-bedd-10b65daa4214 · outbound

This paper cites Adam: a Method for S tochastic Optimization.

Training neural networks without backpropagation using particles Adam: a Method for S tochastic Optimization

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:41.860753Z

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 fedd1859-1872-4aae-ab29-ca280e4e8caa · outbound

This paper cites Multiclass Classiﬁca tion of Dry Beans Using Computer Vision and Machine Learning Techniques.

Training neural networks without backpropagation using particles Multiclass Classiﬁca tion of Dry Beans Using Computer Vision and Machine Learning Techniques

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.558601Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5289b644-d241-4770-ac89-8437d79787cc · outbound

This paper cites Back-Propagation Without Weight Trans- port.

Training neural networks without backpropagation using particles Back-Propagation Without Weight Trans- port

Reference 22

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T20:33:41.016845Z

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 2cbdb36a-5f0e-485a-a1b3-6bcd867e42af · outbound

This paper cites Ensemble Kalman inversion: a derivative-free technique for machine learning tasks.

Training neural networks without backpropagation using particles Ensemble Kalman inversion: a derivative-free technique for machine learning tasks

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:41.838304Z

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 2aff6c4b-5096-4153-8853-b335964f354d · outbound

This paper cites Binary classiﬁca tion posed as a quadratically con- strained quadratic programming and solved using particle s warm optimization.

Training neural networks without backpropagation using particles Binary classiﬁca tion posed as a quadratically con- strained quadratic programming and solved using particle s warm optimization

Reference 24

Resolution: verified exact; doi, observed 2026-08-11T20:33:40.243879Z

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 fb96c42d-4547-4f30-aabb-d623324e99b6 · outbound

This paper cites Quadratically constrained quadratic programming for classification using particle swarms and applications.

Training neural networks without backpropagation using particles Quadratically constrained quadratic programming for classification using particle swarms and applications

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:33:40.910485Z

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 209e67c3-cefc-4ee2-bff3-b19abd922336 · outbound

This paper cites Backpropagation and the br ain.

Training neural networks without backpropagation using particles Backpropagation and the br ain

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.659019Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 114531af-dd01-4862-a1f5-98a51c0c1015 · outbound

This paper cites Random feedback weights support learning in deep neural networks.

Training neural networks without backpropagation using particles Random feedback weights support learning in deep neural networks

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.742757Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 776eba8b-fdb7-4809-95cd-c3ea36c15a57 · outbound

This paper cites Random synaptic feedback w eights support error back- propagation for deep learning.

Training neural networks without backpropagation using particles Random synaptic feedback w eights support error back- propagation for deep learning

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.796834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d8eb611d-4dc1-4709-a0b8-499d947ba60a · outbound

This paper cites Th e HSIC Bottleneck: Deep Learning without Back-Propagation.

Training neural networks without backpropagation using particles Th e HSIC Bottleneck: Deep Learning without Back-Propagation

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:41.813249Z

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 c83366f4-efd4-4eb5-90e2-73d4974a2d13 · outbound

This paper cites Simple Evolutio nary Optimization Can Rival Stochastic Gradient Descent in Neural Networks.

Training neural networks without backpropagation using particles Simple Evolutio nary Optimization Can Rival Stochastic Gradient Descent in Neural Networks

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.827540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b98556f-0a1e-4cd5-8f2f-7173417effce · outbound

This paper cites Random Gradient-Free Min imization of Convex Func- tions.

Training neural networks without backpropagation using particles Random Gradient-Free Min imization of Convex Func- tions

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.833186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 476ba9d3-5399-4c2b-a18d-29d2bccf766c · outbound

This paper cites Derivativ e-free optimization: a review of algo- rithms and comparison of software implementations.

Training neural networks without backpropagation using particles Derivativ e-free optimization: a review of algo- rithms and comparison of software implementations

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.840454Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3576291-6d95-4cd7-9bce-c0b9984a9f2c · outbound

This paper cites Learning representations by back-propagating errors.

Training neural networks without backpropagation using particles Learning representations by back-propagating errors

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:41.788018Z

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 ef2f9b65-d83d-46dd-baa4-43addfb8e24a · outbound

This paper cites Evolution Strategies as a Scalable Alternative to Reinforcement Learning.

Training neural networks without backpropagation using particles Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:41.762619Z

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 b5ceca77-7cfc-42c9-8e52-9d228fce6f42 · outbound

This paper cites Schneider et al.

Training neural networks without backpropagation using particles Schneider et al

Reference 35

Resolution: verified exact; raw_fallback, observed 2026-08-11T20:33:40.695299Z

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 c3954c73-87b3-4f36-b013-a286c05ba88c · outbound

This paper cites Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning.

Training neural networks without backpropagation using particles Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.882224Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc3aa779-b408-4566-86ee-4e5b40092306 · outbound

This paper cites Efﬁcient natural evolution strategies.

Training neural networks without backpropagation using particles Efﬁcient natural evolution strategies

Reference 37

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T20:33:40.565551Z

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 f8f1717b-042e-4951-9d26-690897bb172d · outbound

This paper cites An Evolutionary Algorithm of Linear complexity: Application to Training of Deep Neural Networks.

Training neural networks without backpropagation using particles An Evolutionary Algorithm of Linear complexity: Application to Training of Deep Neural Networks

Reference 38

Resolution: verified exact; local_arxiv, observed 2026-08-11T20:33:40.422167Z

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 f4bd5714-51eb-40b2-898f-b7128350718a · outbound

This paper cites Natural evolution strategies.

Training neural networks without backpropagation using particles Natural evolution strategies

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:41.701603Z

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 f149b44b-376f-4902-be00-bde82454ea65 · outbound

This paper cites Neural Network Learni ng without Backpropagation.

Training neural networks without backpropagation using particles Neural Network Learni ng without Backpropagation

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T20:33:41.670328Z

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 b787b3b8-cedf-43fa-bc84-49ef469a401e · outbound

This paper cites A Gradient-Guided Evolutionar y Approach to Training Deep Neural Networks.

Training neural networks without backpropagation using particles A Gradient-Guided Evolutionar y Approach to Training Deep Neural Networks

Reference 41

Resolution: metadata mismatch; raw_fallback, observed 2026-08-11T20:33:40.389220Z

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 015d7f49-b18e-4d90-9c68-deea9d04f8d5 · outbound

This paper cites an unresolved cited work.

Training neural networks without backpropagation using particles Unresolved cited work

Reference 493

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.240824Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 211b787b-83ab-4581-84c4-ec37c4abbf93 · outbound

This paper cites Evolution Strategies as a Scalable Alternative to Reinforcement Learning.

Training neural networks without backpropagation using particles Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-11T20:33:39.867240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.