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Paper Citation Record · LEDGER

Representation Benefits of Deep Feedforward Networks

As of 5 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 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 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-02T02:48:56.663253Z

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
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

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 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 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-14T23:00:20.720030Z digest=sha256:98c7153e008b0b807b744e021ae871928040131f039c552d273d407991122d27

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 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-10T18:38:54.623816Z digest=sha256:60261cc0903bcb6946aff04847b85c2bf51557b5b9eb1729099716ae04e94ed1

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 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-05-21T05:38:34.370264Z digest=sha256:c29d135fbadb1d6b3df7b05af15c9fa582d3b8c99196477a82226a1b5baeb9d6

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 2026-08-05T06:32:48.257954+00:00.

source=arxiv_source observed=2026-06-27T16:59:34.084575Z digest=sha256:3d9a82d97b9af3828684e917084832eaa7983f87eea56c58038f779b064ee34e

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 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-26T05:19:56.528337Z digest=sha256:a0e4ca3a9b4bdaceee19864079ac04aa756602b396eba3d5c328b9e174ae922f

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 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-07-09T21:48:15.229103Z digest=sha256:76889c8e4d812ab44bb73977b909f32b7e9d5fd780227ff85c6443bd34d3026a

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.

source=arxiv_source observed=2026-07-13T03:41:36.654380Z digest=sha256:f402bdf12841dea3b1efac35cdbb516bab99fb01ad2f6d0ec61112aa27ab4ece

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.

source=pdf_text observed=2026-07-14T10:37:37.440024Z digest=sha256:c977c136087da058120d06e4091d7ba1a305b143e5796232f91026c987095c71

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.

source=arxiv_source observed=2026-08-02T02:48:56.663253Z digest=sha256:27f59e9b5787ef7649953825e6486debdea8d7d7c72410324705fa08a7a4b762