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Paper Citation Record · LEDGER

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing

As of 12 August 2026, this Paper Citation Record lists 21 of 21 outbound references and 0 inbound Pith citation observations for arXiv:2411.14288.

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

pith.paper-citation-record.v1
2411.14288 v2

Coverage vector

measured 21 of 21 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:36:20.103489Z

measured 21 of 21 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

21 of 21 outbound references displayed

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  • verified fuzzy9
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 199dac79-1831-4816-a89e-794f3dd86ccb · outbound

This paper cites Stronger Generalization Bounds for Deep Nets via a Compression Approach.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Stronger Generalization Bounds for Deep Nets via a Compression Approach

Reference 1

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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.

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Observation 623716dd-04ff-47e9-9501-4220d3a940dc · outbound

This paper cites 2c focusing on the models training with the largest dataset sizem = 25600.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing 2c focusing on the models training with the largest dataset sizem = 25600

Reference 3

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

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Observation eb874e93-c9b2-4b53-81a2-52050503d673 · outbound

This paper cites Symmetries and discriminability in feedforward network architectures.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Symmetries and discriminability in feedforward network architectures

Reference 4

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raw_fallback, observed 2026-08-12T15:36:20.239810Z

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.

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Observation b329fc8b-5138-4355-9bed-b91603f223cb · outbound

This paper cites Provably strict generalisation benefit for equivariant models.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Provably strict generalisation benefit for equivariant models

Reference 9

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:36:20.059724Z digest=sha256:3c0dc6acb92201d609383ecdcebebea729a3477783309ea80e1f175646fa9600

Observation 67113ff1-d0ae-4089-b498-64649d0f2f08 · outbound

This paper cites Group invariant scattering.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Group invariant scattering

Reference 11

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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.

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Observation cee1c638-1bdf-4e4d-b612-118e1c5a6b3b · outbound

This paper cites In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors

Reference 12

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local_arxiv, observed 2026-08-12T15:36:20.260969Z

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.

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Observation 27703891-0a63-45e2-9e77-d917b04e13bd · outbound

This paper cites Coordinate Independent Convolutional Networks -- Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Coordinate Independent Convolutional Networks -- Isometry and Gauge Equivariant Convolutions on Riemannian Manifolds

Reference 17

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:36:20.092279Z digest=sha256:7c4116922babf2623b6ebf61bfa08ce056001d783f99874c9d28543e35bddd44

Observation d43d6292-030a-4be3-87f1-bee07d9f2d56 · outbound

This paper cites Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin

Reference 18

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-12T15:36:20.095457Z digest=sha256:a1df633f3739bf493933bf4005387583511cbc9ab36fe7797fc7f2c476eaec41

Observation 0abfeed5-cca5-40a2-a54f-6d5f740a5cc2 · outbound

This paper cites Covering number bounds of certain regularized linear function classes.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Covering number bounds of certain regularized linear function classes

Reference 20

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verified fuzzy
raw_fallback, observed 2026-08-12T15:36:20.316980Z

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.

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Observation 2776c9b5-c62f-4085-8b33-0acb37523af1 · outbound

This paper cites Non-Vacuous Generalisation Bounds for Shallow Neural Networks.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Non-Vacuous Generalisation Bounds for Shallow Neural Networks

Reference 30

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verified exact
local_arxiv, observed 2026-08-12T15:36:20.300703Z

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.

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Observation d8552db8-1331-4f23-98a8-a8718335b2bc · outbound

This paper cites A functional approach to rotation equivariant non-linearities for Tensor Field Networks.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing A functional approach to rotation equivariant non-linearities for Tensor Field Networks

Reference 32

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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.

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Observation f594d360-18c9-45ab-93b9-ee0707e81f5b · outbound

This paper cites A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing A practical method for constructing equivariant multilayer perceptrons for arbitrary matrix groups

Reference 33

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raw_fallback, observed 2026-08-12T15:36:20.354114Z

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.

source=pdf_text observed=2026-08-12T15:36:20.062427Z digest=sha256:0e05a5447881007a7dc545acfa51bfea04dd8a77d39cefc21f37983ec21e55f0

Observation d02a7548-7f5f-432a-8549-5d91d6e1832d · outbound

This paper cites Dynamic routing between capsules.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Dynamic routing between capsules

Reference 1977

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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.

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Observation 7adce3e0-288f-4955-a8c4-cc13fec9a317 · outbound

This paper cites The role of invariance in spectral complexity-based generalization bounds.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing The role of invariance in spectral complexity-based generalization bounds

Reference 1989

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local_arxiv, observed 2026-08-12T15:36:20.249855Z

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.

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Observation 67d0d8cb-ca07-42ea-97d1-4af1f55149e0 · outbound

This paper cites On the Benefits of Invariance in Neural Networks.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing On the Benefits of Invariance in Neural Networks

Reference 2011

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Unavailable: canonical work link unavailable.

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Observation 53e120c2-2435-4385-9cbd-1fe6c055543c · outbound

This paper cites Theoretical Analysis of Inductive Biases in Deep Convolutional Networks.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Theoretical Analysis of Inductive Biases in Deep Convolutional Networks

Reference 2017

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:36:20.098137Z digest=sha256:c0f0bc39d78ffd435c21fc5a278fd81e6dc56f2a2fd07a2a6d93d40b9f59280c

Observation ae25e59d-356f-4fc7-a805-13ad8e904755 · outbound

This paper cites Generalization bounds for deep convolutional neural networks.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Generalization bounds for deep convolutional neural networks

Reference 2018

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source=pdf_text observed=2026-08-12T15:36:20.067992Z digest=sha256:7bd47d97f204bd1a5ce59fca0af62b821db02d674b4cbb76beb7ebbafa049bfc

Observation f2ec5a2e-55ea-454a-9a3a-a6dcfe67b189 · outbound

This paper cites Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data

Reference 2019

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Observation 5943d167-696c-4a42-a463-2c0f7c08cbc4 · outbound

This paper cites On the generalization of equivariance and convolution in neural networks to the action of compact groups.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing On the generalization of equivariance and convolution in neural networks to the action of compact groups

Reference 2021

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verified fuzzy
raw_fallback, observed 2026-08-12T15:36:20.346693Z

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.

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Observation 4821418a-ea13-47de-af68-fa2b7006bc8e · outbound

This paper cites Group Equivariant Convolutional Networks.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Group Equivariant Convolutional Networks

Reference 2022

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Unavailable: canonical work link unavailable.

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Observation 49259aea-b60c-4bf4-9395-0a8111a2d313 · outbound

This paper cites Uncertainty Principles and Signal Recovery.

On the Sample Complexity of One Hidden Layer Networks with Equivariance, Locality and Weight Sharing Uncertainty Principles and Signal Recovery

Reference 8441

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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.

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