Paper Citation Record · LEDGER

Representation Benefits of Deep Feedforward Networks

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:1509.08101.

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

pith.paper-citation-record.v1
1509.08101 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:37:33.184516Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T21:56:38.427283Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 1eb6eeab-64fc-4946-8732-f975bad9bbdd · inbound

Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization cites this paper.

Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization Representation Benefits of Deep Feedforward Networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-14T11:19:57.586021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 093545be-aaf5-4637-acdc-9cf471313751 · inbound

Automated Architecture Design for Deep Neural Networks cites this paper.

Automated Architecture Design for Deep Neural Networks Representation Benefits of Deep Feedforward Networks

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-14T11:53:19.501997Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 816081eb-5167-4dc5-b6a5-d430bb2e3465 · inbound

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models cites this paper.

Information-Theoretic Lower Bounds for Compressive Sensing with Generative Models Representation Benefits of Deep Feedforward Networks

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-14T10:50:12.388369Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdb41f68-0554-4543-bf03-8457e7adcddd · inbound

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models cites this paper.

Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models Representation Benefits of Deep Feedforward Networks

Reference 189

Resolution: metadata mismatch; local_arxiv, observed 2026-05-14T23:00:21.238791Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b75f4a21-23f1-4bd3-b15b-32412ddeeb33 · inbound

GradAlign for Training-free Model Performance Inference cites this paper.

GradAlign for Training-free Model Performance Inference Representation Benefits of Deep Feedforward Networks

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-12T05:52:21.956430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88251523-fb4e-428b-b060-eca9597b8be0 · inbound

Simulating Hard Attention Using Soft Attention cites this paper.

Simulating Hard Attention Using Soft Attention Representation Benefits of Deep Feedforward Networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-11T16:45:21.679501Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de9bcbd7-8140-47b4-80aa-8bd4ea12206e · inbound

On Space Folds of ReLU Neural Networks cites this paper.

On Space Folds of ReLU Neural Networks Representation Benefits of Deep Feedforward Networks

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T20:07:26.322010Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2967f474-e37f-4a87-9638-cf1e0d55d5b6 · inbound

Non-identifiability distinguishes Neural Networks among Parametric Models cites this paper.

Non-identifiability distinguishes Neural Networks among Parametric Models Representation Benefits of Deep Feedforward Networks

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-16T10:37:33.184516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6705eab-cd56-4152-84dc-f8e0464870f3 · inbound

Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective cites this paper.

Exploring and Improving Initialization for Deep Graph Neural Networks: A Signal Propagation Perspective Representation Benefits of Deep Feedforward Networks

Reference 116

Resolution: unresolved; no resolver link, observed 2026-08-15T19:24:57.622166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2323a4c-a901-47f1-892c-6bbc021ac023 · inbound

On the algorithmic construction of deep ReLU networks cites this paper.

On the algorithmic construction of deep ReLU networks Representation Benefits of Deep Feedforward Networks

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T18:46:22.919591Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 182acd69-f219-49d4-8b02-11204011a320 · inbound

ReLU Networks for Exact Generation of Similar Graphs cites this paper.

ReLU Networks for Exact Generation of Similar Graphs Representation Benefits of Deep Feedforward Networks

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:15:51.077355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d3d4245c-508e-41d5-899b-d14e22286017 · inbound

Approximation Theory for Neural Networks: Old and New cites this paper.

Approximation Theory for Neural Networks: Old and New Representation Benefits of Deep Feedforward Networks

Reference 55

Resolution: verified exact; local_arxiv, observed 2026-05-21T05:39:40.427436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c4e476b4-a490-4d8a-b755-f85658db17a4 · inbound

A Theory on Flow Matching with Neural Networks cites this paper.

A Theory on Flow Matching with Neural Networks Representation Benefits of Deep Feedforward Networks

Reference 215

Resolution: metadata mismatch; local_arxiv, observed 2026-07-03T00:47:30.836559Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 13295d5b-952b-4ae8-84fe-f20c5656872d · inbound

Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation cites this paper.

Algorithmic Foundations of Deep Learning: Complexity-Theoretic Rates and a Characterization of Universal Approximation Representation Benefits of Deep Feedforward Networks

Reference 31

Resolution: metadata mismatch; local_arxiv, observed 2026-07-04T13:19:50.632516Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a2c15abd-12fc-4bb5-aad0-41e6f4f18923 · inbound

Local large deviations for linear-region growth in random piecewise-linear networks cites this paper.

Local large deviations for linear-region growth in random piecewise-linear networks Representation Benefits of Deep Feedforward Networks

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-07-09T21:56:38.428382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 32974cc6-3126-4e44-8849-afa0273615ed · inbound

The Cost of Discretization in Functional Linear Regression: Minimax Rates and Adaptation cites this paper.

The Cost of Discretization in Functional Linear Regression: Minimax Rates and Adaptation Representation Benefits of Deep Feedforward Networks

Reference 113

Resolution: unresolved; no resolver link, observed 2026-07-13T03:41:36.654380Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 60dc5fc2-39ff-4e12-bd76-292730487ad4 · inbound

Approximation of Analytic Functions by ReLU Neural Networks with Adjustable Depth and Width cites this paper.

Approximation of Analytic Functions by ReLU Neural Networks with Adjustable Depth and Width Representation Benefits of Deep Feedforward Networks

Reference 52

Resolution: unresolved; no resolver link, observed 2026-07-14T10:37:37.440024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b9a9e38-0e38-4c2c-b320-d3f9da8a1a17 · inbound

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks cites this paper.

LIGO-PINN: Learned Initialization via Gated Optimization to Alleviate Convergence Failures in Physics Informed Neural Networks Representation Benefits of Deep Feedforward Networks

Reference 106

Resolution: unresolved; no resolver link, observed 2026-08-02T02:48:56.663253Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.