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Paper Citation Record · LEDGER

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks

As of 17 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 2 inbound Pith citation observations for arXiv:2505.11589.

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

pith.paper-citation-record.v1
2505.11589 v1

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:56:01.586850Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-30T22:37:13.002522Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T13:55:45.491954Z

Reference resolution

43 of 43 outbound references displayed

  • verified exact5
  • verified fuzzy21
  • unresolved17
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6269d8ed-3079-47fb-b2a8-2c6e418d81f5 · outbound

This paper cites https://www.hhs.gov/hipaa/, 1996.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks https://www.hhs.gov/hipaa/, 1996

Reference 1

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raw_fallback, observed 2026-08-15T20:56:02.399063Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.403827Z digest=sha256:7186fdf1084b25c78c9b14602d891776200cd8eb1c34a7612fb6986f3d5d409e

Observation bbc023e2-6063-4462-b5de-c3c9ca54d8b2 · outbound

This paper cites http://data.europa.eu/eli/reg/2016/679/oj, 2016.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks http://data.europa.eu/eli/reg/2016/679/oj, 2016

Reference 2

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raw_fallback, observed 2026-08-15T20:56:02.386379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.409772Z digest=sha256:2030ea937a6ed3aba20bbe320085678d2c3e924e2cbc446fe800c56d6e07d6c1

Observation 45becb5b-ef18-4a06-80a9-9422e6440a16 · outbound

This paper cites Stabilizing Inputs to Approximated Nonlinear Functions for Inference with Homomorphic Encryption in Deep Neural Networks.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Stabilizing Inputs to Approximated Nonlinear Functions for Inference with Homomorphic Encryption in Deep Neural Networks

Reference 3

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local_arxiv, observed 2026-08-15T20:56:02.083376Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.414308Z digest=sha256:0762897b6fbc803921b77e23d26386a6a84901937fc58d4fb97fbdcd7deda4a8

Observation 6d70c6ef-0e97-4b08-bd7c-dfb0fa1c215f · outbound

This paper cites On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks On Polynomial Approximations for Privacy-Preserving and Verifiable ReLU Networks

Reference 4

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no resolver link, observed 2026-08-15T20:56:01.420217Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:01.420217Z digest=sha256:a9dd7cc19bddc71ab5aee668dd4d28829d40cedd40151e2630fad0d82ae90945

Observation 1933d993-922f-4f87-9ffd-222e273cecd9 · outbound

This paper cites OpenFHE : Open-source fully homomorphic encryption library.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks OpenFHE : Open-source fully homomorphic encryption library

Reference 5

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raw_fallback, observed 2026-08-15T20:56:02.373484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.425115Z digest=sha256:fb14b7a8943726331a3ac4dc582811ad76ececc091daaf709bc87b83c6e308e5

Observation e82395b3-af08-4beb-b608-a579d44141c8 · outbound

This paper cites A methodology for training homomorphic encryption friendly neural networks.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks A methodology for training homomorphic encryption friendly neural networks

Reference 6

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raw_fallback, observed 2026-08-15T20:56:02.360046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.429587Z digest=sha256:15d63dbf66eae5b50b8987f6da78cda9bb4a02410a7793ed3ac5347523cc9515

Observation ca3b1825-4386-4f80-a6b4-9e7d9ecdc46d · outbound

This paper cites Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Training Large Scale Polynomial CNNs for E2E Inference over Homomorphic Encryption

Reference 7

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:01.434090Z digest=sha256:f7dc587717cdef09276657847f17a30ea199d2dcce4efb8184d2cf9a955860b1

Observation 22cea893-1478-4bf7-979c-ffc69a0afcb9 · outbound

This paper cites nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks nGraph-HE2: A High-Throughput Framework for Neural Network Inference on Encrypted Data

Reference 8

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local_arxiv, observed 2026-08-15T20:56:02.039047Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.439774Z digest=sha256:a8d196adb7b9ab5c72363bcd585839378aff997d8d7215d83ebe77f7d16c88f2

Observation 9a62f445-ecfe-4f6d-88c6-f6a963c21af8 · outbound

This paper cites Low latency privacy preserving inference.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Low latency privacy preserving inference

Reference 9

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raw_fallback, observed 2026-08-15T20:56:02.347400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.443829Z digest=sha256:23e885e15cb80e963ab878121410e7ec27b6fc1470df54c175b2ca31fd2cee52

Observation 85f94d51-e455-4c01-9579-f5c5422cd696 · outbound

This paper cites Capture-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Capture-24: A large dataset of wrist-worn activity tracker data collected in the wild for human activity recognition

Reference 10

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raw_fallback, observed 2026-08-15T20:56:02.335001Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.448161Z digest=sha256:7b0e8d89e648e785993b21e413a797bce0aba96fcb9064b2da898133051d9b7e

Observation 6ed75fd9-519e-4d44-9f72-8e1b00b3fe4a · outbound

This paper cites Homomorphic encryption for arithmetic of approximate numbers.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Homomorphic encryption for arithmetic of approximate numbers

Reference 11

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raw_fallback, observed 2026-08-15T20:56:02.321794Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.452276Z digest=sha256:9a7924771e2021ca7bf5e924b3fc7d4f658bbbb82eae61a31f81e13887cbf8fb

Observation 1b53140e-0f4c-4d09-bb17-cfc1ea9bfcec · outbound

This paper cites Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Yannis Panagakis, Jiankang Deng, and Stefanos Zafeiriou.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Chrysos, Stylianos Moschoglou, Giorgos Bouritsas, Yannis Panagakis, Jiankang Deng, and Stefanos Zafeiriou

Reference 12

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raw_fallback, observed 2026-08-15T20:56:02.309291Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.456848Z digest=sha256:ced62ce23f0b7428655f265329d5f39ed08b69fb3163599409faf2713f264844

Observation 10e910a1-c9c3-48cb-92c5-332219328e4f · outbound

This paper cites The mnist database of handwritten digit images for machine learning research.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks The mnist database of handwritten digit images for machine learning research

Reference 13

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no resolver link, observed 2026-08-15T20:56:01.460583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:01.460583Z digest=sha256:9a3d1126faf40790e738bc8962d9d16e2b2cbec7238c9a58fbfd0fe0f83b5c6e

Observation f01026af-98ca-4500-bb08-220f82579667 · outbound

This paper cites Cryptonets: applying neural networks to encrypted data with high throughput and accuracy.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Cryptonets: applying neural networks to encrypted data with high throughput and accuracy

Reference 14

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raw_fallback, observed 2026-08-15T20:56:02.286420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.464746Z digest=sha256:4f7c7e553e9a4b88e812d7f8ee60ed18764e0865e5cc4935d053c35620ba6a45

Observation 1220f43c-6dfe-4a1d-8a4f-2244adeac1e0 · outbound

This paper cites Scalable Interpretability via Polynomials.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Scalable Interpretability via Polynomials

Reference 15

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local_arxiv, observed 2026-08-15T20:56:01.671588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.469054Z digest=sha256:17e99fba869d69fac303fe12217af92bcc3f28843c882dfb496cac48995986b6

Observation b4d8d4d6-7fa3-45cf-b232-91ac52aea5f1 · outbound

This paper cites A new remez-type algorithm for best polynomial approximation.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks A new remez-type algorithm for best polynomial approximation

Reference 16

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raw_fallback, observed 2026-08-15T20:56:02.270758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.473265Z digest=sha256:f76a2390208e5a3e9e3f8f1608431c2094f70e42631fccab803daa5a29cfac40

Observation 345f5721-91fc-4b75-ab88-2cf69efa5bff · outbound

This paper cites Interpretable polynomial neural ordinary differential equations.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Interpretable polynomial neural ordinary differential equations

Reference 17

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raw_fallback, observed 2026-08-15T20:56:02.257432Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.477688Z digest=sha256:78835d224bfa6609528c509afd79126d85a2e1070910966531d16323c7b37a81

Observation 6ec59626-46dc-4d54-8a2d-ad235dabd788 · outbound

This paper cites Polynomial activation functions.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Polynomial activation functions

Reference 18

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raw_fallback, observed 2026-08-15T20:56:02.245413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.482809Z digest=sha256:56d68d42dc7259e857e6ba6e22be10b3db82a978a960befe73cb4b20961a42f4

Observation 0a986940-2b8d-400e-bc2f-06d11a822ec4 · outbound

This paper cites Improved polynomial neural networks with normalised activations.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Improved polynomial neural networks with normalised activations

Reference 19

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no resolver link, observed 2026-08-15T20:56:01.487179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:01.487179Z digest=sha256:eb41b2b885539c9a9a227514138d03579a83caa4c4be88a9fd0333865b8017bb

Observation df023d95-682f-4520-8ab6-cf6cf7ac29a9 · outbound

This paper cites Zhang, Shaoqing Ren, and Jian Sun.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Zhang, Shaoqing Ren, and Jian Sun

Reference 20

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raw_fallback, observed 2026-08-15T20:56:02.233995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.491759Z digest=sha256:16fdec4b77f6c640fb9f5bc364a1af992e42a423b6c40bfc263382668005b577

Observation 9fcb701d-abca-4e7f-b29a-a2d1290effac · outbound

This paper cites CryptoDL: Deep Neural Networks over Encrypted Data.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks CryptoDL: Deep Neural Networks over Encrypted Data

Reference 21

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no resolver link, observed 2026-08-15T20:56:01.496232Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:01.496232Z digest=sha256:ec74a1ac458cf5928c054ebf99427f5310e33b21f9fccbe485f4b1683c7a84ad

Observation 92c0ea08-3000-448e-8145-558f1533941c · outbound

This paper cites Stinchcombe, and Halbert L.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Stinchcombe, and Halbert L

Reference 22

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no resolver link, observed 2026-08-15T20:56:01.501340Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:01.501340Z digest=sha256:37b7c53a60fc8efab6794efc3dd1817c13aa3e7459a493a49489bc942104ed6e

Observation 43198825-9346-49bd-9e99-ab9d085bcf54 · outbound

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

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 23

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no resolver link, observed 2026-08-15T20:56:01.505131Z

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Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:01.505131Z digest=sha256:915ff1f8c659ab6d8c37c57c04b12268f978dfd5be7efbfc13d34b7c12e24999

Observation ce8660ca-23f4-47d7-bc74-6fd7ec1b8d9f · outbound

This paper cites Highly accurate cnn inference using approximate activation functions over homomorphic encryption.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Highly accurate cnn inference using approximate activation functions over homomorphic encryption

Reference 24

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verified fuzzy
raw_fallback, observed 2026-08-15T20:56:02.213419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.509110Z digest=sha256:7e3cef5c22ae84f4a61d913f16623842c55d8c65c1447b57b6271c80f0091e01

Observation b6e45fd2-e068-4512-b0fa-a7e039dd9ded · outbound

This paper cites GAZELLE : A low latency framework for secure neural network inference.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks GAZELLE : A low latency framework for secure neural network inference

Reference 25

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verified fuzzy
raw_fallback, observed 2026-08-15T20:56:02.201135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.512825Z digest=sha256:387cfbdcca7a7428751319bd5ee94602ef803fcd792ffe81b097f56c784b3feb

Observation 2d3cfee1-3304-4cf7-8910-0a90666052c1 · outbound

This paper cites Universal Approximation with Deep Narrow Networks.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Universal Approximation with Deep Narrow Networks

Reference 26

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no resolver link, observed 2026-08-15T20:56:01.516594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:01.516594Z digest=sha256:9667c3116efda5eadd7b823b7e2be795c9645801cd627ef821905bca606907dc

Observation 5ef0a224-1688-4933-8160-9287a9387f06 · outbound

This paper cites On the expressive power of deep polynomial neural networks.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks On the expressive power of deep polynomial neural networks

Reference 27

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raw_fallback, observed 2026-08-15T20:56:02.181922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.521241Z digest=sha256:38b25e4a8390542f6ab49f3cd83a1c28b56d0c500c417c71d45a2bfc7d9e1d32

Observation 8acb6cfc-d6cf-4f32-b524-35991fb715c7 · outbound

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

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Learning multiple layers of features from tiny images

Reference 28

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no resolver link, observed 2026-08-15T20:56:01.525314Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:01.525314Z digest=sha256:f3fe52fd67a062a1e3b6942aa69dd05029cdc95c4403bd82099e047bf8f72f17

Observation 90ee0873-8d47-44ac-9e61-3634ff5757b9 · outbound

This paper cites Cifar-100 (canadian institute for advanced research).

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Cifar-100 (canadian institute for advanced research)

Reference 29

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no resolver link, observed 2026-08-15T20:56:01.530380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:01.530380Z digest=sha256:8fb6ca6dfbc389c1f0a61cd9a1021bcc68c3e62e8fa877701efab4262bb2ae15

Observation e659526e-56e2-4568-a644-aa55d79313bd · outbound

This paper cites Precise approximation of convolutional neural networks for homomorphically encrypted data.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Precise approximation of convolutional neural networks for homomorphically encrypted data

Reference 30

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verified fuzzy
raw_fallback, observed 2026-08-15T20:56:02.152908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.534547Z digest=sha256:765a37163cf568e72364ac1efc7e0bfe3976c28a30ddbdba3ffe12fc2f477272

Observation 72fb29c9-c677-4f07-b6d8-dc6114069799 · outbound

This paper cites Optimized layerwise approximation for efficient private inference on fully homomorphic encryption, 2024.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Optimized layerwise approximation for efficient private inference on fully homomorphic encryption, 2024

Reference 31

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no resolver link, observed 2026-08-15T20:56:01.538726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:01.538726Z digest=sha256:568536f71ab015d90614421633621c52b2cc6b9e6e2a4f33c28a90f795145d93

Observation 01ba7b84-619e-491e-b9fc-05d80238c51a · outbound

This paper cites Decoupled weight decay regularization.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Decoupled weight decay regularization

Reference 32

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raw_fallback, observed 2026-08-15T20:56:02.140037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.542438Z digest=sha256:496c42f7b1eb9f4cc70adaedcdeb43c7253c74ec6221072106e86cef11d0584f

Observation 1965d21a-4a76-47aa-8de3-e829b9d02901 · outbound

This paper cites an unresolved cited work.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Unresolved cited work

Reference 33

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doi, observed 2026-08-15T20:56:01.652973Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.545979Z digest=sha256:ba0eebc24cb6f0bf2fdd9790e0bc5defe09a9d8dfa2a24fe812626975bc1b03c

Observation a173cf94-5214-4a20-afeb-da4f50ba2bb5 · outbound

This paper cites Trefethen.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Trefethen

Reference 34

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raw_fallback, observed 2026-08-15T20:56:02.127993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-08-15T20:56:01.550034Z digest=sha256:a923ffdea29c8838c52d6577af578490cc5041b3cfe085a272dfd96522eeee64

Observation 058db889-b768-4322-9bf4-6baf69b74958 · outbound

This paper cites AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks AESPA: Accuracy Preserving Low-degree Polynomial Activation for Fast Private Inference

Reference 35

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no resolver link, observed 2026-08-15T20:56:01.555163Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T20:56:01.555163Z digest=sha256:a87e6c915b60a17c22a69ebac27ce759befea59f398ef9ade50384e284ba2e97

Observation 11a1a3bd-27e0-46fc-ac32-e24d189e7635 · outbound

This paper cites Self-learning activation functions to increase accuracy of privacy-preserving convolutional neural networks with homomorphic encryption.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Self-learning activation functions to increase accuracy of privacy-preserving convolutional neural networks with homomorphic encryption

Reference 36

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Observation fde89be1-e3d2-4b01-a1cc-1d57ef8dc496 · outbound

This paper cites Human Activity Recognition Using Smartphones.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Human Activity Recognition Using Smartphones

Reference 37

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source=arxiv_source observed=2026-08-15T20:56:01.563473Z digest=sha256:ee0fb3e5ea66539631bb1154bb136d357f3aba27387b05ad7181c628492c591f

Observation 7e8bcd8b-218e-4b4c-bb0d-dccd6668fbc1 · outbound

This paper cites Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition

Reference 38

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source=arxiv_source observed=2026-08-15T20:56:01.567406Z digest=sha256:a64e2e2cf13623809cd61614cf8d5c2d865c666b630096e3becf5df3dab0a601

Observation 0580d3f4-ea2f-4a55-8e8a-e5f6042864c4 · outbound

This paper cites Ppolynets: Achieving high prediction accuracy and efficiency with parametric polynomial activations.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Ppolynets: Achieving high prediction accuracy and efficiency with parametric polynomial activations

Reference 39

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source=arxiv_source observed=2026-08-15T20:56:01.571267Z digest=sha256:f50a1a9ff4895cc254c7182ea631cb55dc11fee4f314f3031834fc680dd36aa9

Observation 251aeaad-5bda-4e85-841e-83a8330b2242 · outbound

This paper cites Extrapolation of polynomial nets and their generalization guarantees, 2022.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Extrapolation of polynomial nets and their generalization guarantees, 2022

Reference 40

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raw_fallback, observed 2026-08-15T20:56:02.115641Z

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source=arxiv_source observed=2026-08-15T20:56:01.575452Z digest=sha256:bf00eb205946a0d840cc123aabf6b0d6f409fa41f897cf9b966654208807b05d

Observation e5525528-f5d7-487b-9dc4-6903c5c3e269 · outbound

This paper cites Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017

Reference 41

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source=arxiv_source observed=2026-08-15T20:56:01.579335Z digest=sha256:9b49aba3dfd63fa373ccebcadc1fb8b4aced31b6900df49c336b6a403d8020bc

Observation 74eb3189-1c9d-404d-8d73-7fc855f1b184 · outbound

This paper cites Polynomial activation neural networks: Modeling, stability analysis and coverage bp-training.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Polynomial activation neural networks: Modeling, stability analysis and coverage bp-training

Reference 42

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source=arxiv_source observed=2026-08-15T20:56:01.583044Z digest=sha256:3121885eb66e8ef66255bd476b967457737eed98e090527148383746e2fd3acb

Observation 4d107f10-fa5e-41a5-a054-c0500f1da844 · outbound

This paper cites Converting transformers to polynomial form for secure inference over homomorphic encryption, 2023.

A Training Framework for Optimal and Stable Training of Polynomial Neural Networks Converting transformers to polynomial form for secure inference over homomorphic encryption, 2023

Reference 43

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raw_fallback, observed 2026-08-15T20:56:02.097091Z

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source=arxiv_source observed=2026-08-15T20:56:01.586850Z digest=sha256:a6b47eae8060bf902a81bc6c1c3f11b56a0d1c91492646b2b312d76992c20c6e

Pith citing papers

Observation 98e168f1-a45d-418f-b5fa-6d4ac9fea195 · inbound

Minimal Filling Architectures of Polynomial Neural Networks: Counterexamples, Frontier Search, and Defects cites this paper.

Minimal Filling Architectures of Polynomial Neural Networks: Counterexamples, Frontier Search, and Defects A Training Framework for Optimal and Stable Training of Polynomial Neural Networks

Reference 7

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arxiv_id, observed 2026-05-12T07:41:31.829274Z

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source=arxiv_source observed=2026-05-12T02:23:10.600301Z digest=sha256:f720e141cd680ec69f7bb863357b53b7e0fe58d68c292101e8fa462b4c5133f3

Observation c0a246ae-8607-4986-988b-b36017419078 · inbound

Minimal Filling Architectures of Polynomial Neural Networks: Counterexamples, Frontier Search, and Defects cites this paper.

Minimal Filling Architectures of Polynomial Neural Networks: Counterexamples, Frontier Search, and Defects A Training Framework for Optimal and Stable Training of Polynomial Neural Networks

Reference 7

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arxiv_id, observed 2026-07-01T13:55:45.493334Z

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source=arxiv_source observed=2026-06-30T22:37:13.002522Z digest=sha256:8494a98dabdb28500efd8d6c87deff075e5bbc842b62628d4b969d0fcd7e2255