Pith. sign in

Paper Citation Record · LEDGER

The Geometry of ReLU Networks through the ReLU Transition Graph

As of 20 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 1 inbound Pith citation observation for arXiv:2505.11692.

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

pith.paper-citation-record.v1
2505.11692 v2

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:55:55.250912Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:37:56.522303Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T18:16:14.067578Z

Reference resolution

23 of 23 outbound references displayed

  • verified exact0
  • verified fuzzy15
  • unresolved7
  • parse uncertain1
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5cc9309a-15c7-4e52-a7ae-f497c3ea9a8b · outbound

This paper cites Understanding deep neural networks with rectified linear units, 2018.

The Geometry of ReLU Networks through the ReLU Transition Graph Understanding deep neural networks with rectified linear units, 2018

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-15T20:55:55.181218Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:55:55.181218Z digest=sha256:e3ad70c262a0f660c4afda756d1d2dd82bb487c7047e6e427171b025f6376809

Observation a6441b2d-0fae-4df6-acc0-5812b94c78f0 · outbound

This paper cites Bartlett.

The Geometry of ReLU Networks through the ReLU Transition Graph Bartlett

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.471757Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.185190Z digest=sha256:c7d62ca7cbc7defbb226a809ed6123f1e5c093fe80eb3458a3f08679061cf3d8

Observation 3a19054a-7b7c-4b76-8edf-a370a60d5e17 · outbound

This paper cites A combinatorial theory of dropout: Subnetworks, graph geometry, and generalization, 2025.

The Geometry of ReLU Networks through the ReLU Transition Graph A combinatorial theory of dropout: Subnetworks, graph geometry, and generalization, 2025

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-15T20:55:55.188410Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:55:55.188410Z digest=sha256:7735d08a1fa5328d53c21ac727622ce204ac74457b220d9eb29102e2648b8f60

Observation 4057ae10-710a-4a31-8d5d-d638432730a7 · outbound

This paper cites Neural networks as universal finite-state machines: A constructive deterministic finite automaton theory, 2025.

The Geometry of ReLU Networks through the ReLU Transition Graph Neural networks as universal finite-state machines: A constructive deterministic finite automaton theory, 2025

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.456407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.191650Z digest=sha256:66bda86792e6b2c9fc83af4a9749128d1426f29b588cd2063513723d8d6c23be

Observation e21063ec-0259-469d-8475-9b3bae75d670 · outbound

This paper cites The lottery ticket hypothesis: Finding sparse, trainable neural networks, 2019.

The Geometry of ReLU Networks through the ReLU Transition Graph The lottery ticket hypothesis: Finding sparse, trainable neural networks, 2019

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-15T20:55:55.194695Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:55:55.194695Z digest=sha256:0130596c941cee292ded5b33890ded29d9a19fd735d0e025206ee20f9d038193

Observation 11bcba2c-2daf-4cf8-ad50-0997ed8e52c8 · outbound

This paper cites Deep sparse rectifier neural networks.

The Geometry of ReLU Networks through the ReLU Transition Graph Deep sparse rectifier neural networks

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.440544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.198337Z digest=sha256:e64a5d23e4b0cd252cff182cbed8c7ab7a3156afa1ddd06150fcc90853d697ba

Observation 0b7d623d-a732-4eb1-9fc5-4970be1a2ddf · outbound

This paper cites Guss and Ruslan Salakhutdinov.

The Geometry of ReLU Networks through the ReLU Transition Graph Guss and Ruslan Salakhutdinov

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-15T20:55:55.201608Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:55:55.201608Z digest=sha256:aeb7ede6f173f17634718d669cef450543f6ba273b55be993c76a16f8c79963e

Observation 617dac91-d724-4c17-84b8-128dc3b2cc84 · outbound

This paper cites Complexity of linear regions in deep networks.

The Geometry of ReLU Networks through the ReLU Transition Graph Complexity of linear regions in deep networks

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.424161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.204473Z digest=sha256:f83a0b1e71c08903a56989abcfd12c8b08bbdbf7b742a21dd18240a1d9f15c0f

Observation d6d92211-ad06-41ca-a75f-3f3964ef3bc6 · outbound

This paper cites Approximating continuous functions by relu nets of minimal width, 2018.

The Geometry of ReLU Networks through the ReLU Transition Graph Approximating continuous functions by relu nets of minimal width, 2018

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.407316Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.210622Z digest=sha256:2e7bb83ecbbcdd50068034cd8ca8f243cd8de78aa2daa0774679f98b66202261

Observation ecc44947-6d2f-4910-a230-ba38856f4c23 · outbound

This paper cites On the number of linear regions of deep neural networks.

The Geometry of ReLU Networks through the ReLU Transition Graph On the number of linear regions of deep neural networks

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.397310Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.213642Z digest=sha256:b7e8c08d02ae7c84e7f93bd543564fac8429af5dabaa3f6862b782e7831154a0

Observation 7a38f95d-497e-41d0-9084-ec54d9d0d742 · outbound

This paper cites an unresolved cited work.

The Geometry of ReLU Networks through the ReLU Transition Graph Unresolved cited work

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-15T20:55:55.216722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:55:55.216722Z digest=sha256:425451689d7681306ad196e2d36b3e0df8781d3a4eec580cd1431ecdf89b3091

Observation 99209b28-05b3-427d-9162-b9b2067bd561 · outbound

This paper cites In search of the real inductive bias: On the role of implicit regularization in deep learning, 2015.

The Geometry of ReLU Networks through the ReLU Transition Graph In search of the real inductive bias: On the role of implicit regularization in deep learning, 2015

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-15T20:55:55.219906Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:55:55.219906Z digest=sha256:75328e7bb64c860601a8ce24743c9e978e58b61a8e150e0740736f2260cfa8d0

Observation 4192890c-1eb5-4e88-80b2-8fa584acf127 · outbound

This paper cites Norm-based capacity control in neural networks.

The Geometry of ReLU Networks through the ReLU Transition Graph Norm-based capacity control in neural networks

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-15T20:55:55.223050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:55:55.223050Z digest=sha256:c699d9b2fe6351ce03dce72f82a4d7390a9e6fd22e12d22dd6bc9b0a2d73a5af

Observation f68ed799-33a6-4ad9-9647-d5a92648c5bf · outbound

This paper cites Abolafia, Jeffrey Pennington, and Jascha Sohl- Dickstein.

The Geometry of ReLU Networks through the ReLU Transition Graph Abolafia, Jeffrey Pennington, and Jascha Sohl- Dickstein

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.367819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.226185Z digest=sha256:f3908d746a01816e0c9dbff94d6f8cebd00f93b3a959ad5d316443cc0bf4e24d

Observation 5d9b1354-2e22-4517-bcb0-bf2f4ee8b1ea · outbound

This paper cites Pytorch: An imperative style, high- performance deep learning library.

The Geometry of ReLU Networks through the ReLU Transition Graph Pytorch: An imperative style, high- performance deep learning library

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.357927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.229318Z digest=sha256:53b373286f55fbd815bb9bebe3dd31bc4adefb2b37f0f978e6102f111b0ade02

Observation b6ee4102-6fd0-42a8-b11a-b93b05da5ab3 · outbound

This paper cites Expo- nential expressivity in deep neural networks through transient chaos.

The Geometry of ReLU Networks through the ReLU Transition Graph Expo- nential expressivity in deep neural networks through transient chaos

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.348219Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.232241Z digest=sha256:77993f39b069d13b515f93cdc79e61b5613e1fc620dfbd77f2772c712e2bff36

Observation 2c165f70-01c6-4c52-88c0-5d62d015b6b5 · outbound

This paper cites On the expressive power of deep neural networks, 2017.

The Geometry of ReLU Networks through the ReLU Transition Graph On the expressive power of deep neural networks, 2017

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.336475Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.235171Z digest=sha256:b423d7bd5972cdebe0475f214b2d39a50a2be8fa5d721506849eadf2f9221930

Observation e65edef5-6a0b-4140-bdd1-de798fe798fc · outbound

This paper cites Bounding and counting linear regions of deep neural networks.

The Geometry of ReLU Networks through the ReLU Transition Graph Bounding and counting linear regions of deep neural networks

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.325545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.238235Z digest=sha256:b417412485cd5e786b20aba81b9a8e447f9b5fed54bb2efac52b583bf83795d9

Observation e91560f7-4d1e-4ab1-a79d-703525800f32 · outbound

This paper cites benefits of depth in neural networks.

The Geometry of ReLU Networks through the ReLU Transition Graph benefits of depth in neural networks

Reference 19

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.313688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.241242Z digest=sha256:01f4ff506da57bfaccdd99375c73e89b37aba106ffe84c558e5dcc1b508b1425

Observation 95b140b0-0f27-4f6a-84f3-3bd350f84044 · outbound

This paper cites Facing up to arrangements: face-count formulas for partitions of space by hyperplanes, volume 1.

The Geometry of ReLU Networks through the ReLU Transition Graph Facing up to arrangements: face-count formulas for partitions of space by hyperplanes, volume 1

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.304083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.244296Z digest=sha256:f08c1c94cf92ae1f9b96f0fca8d22c69b3e5310eb90bccbbdb5b1cec382204fb

Observation 1736ffe8-064a-4457-ab46-bdd0204f9da8 · outbound

This paper cites Lee, Martin J.

The Geometry of ReLU Networks through the ReLU Transition Graph Lee, Martin J

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.293450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.247559Z digest=sha256:bbf105c66c562536d03b190d4d087cb53ed40bb581c6e79a2d1833e87342f799

Observation adac6c60-c66b-463f-bf89-45fa71715912 · outbound

This paper cites For each such nodev∈ S, the corresponding region Rv contributes little to the function’s global variation due to its low connectivity (few adjacent regions) and likely small volume.

The Geometry of ReLU Networks through the ReLU Transition Graph For each such nodev∈ S, the corresponding region Rv contributes little to the function’s global variation due to its low connectivity (few adjacent regions) and likely small volume

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T20:55:55.282729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T20:55:55.250912Z digest=sha256:b0d6e928bef7c19859bd57233f6786a1bb869fbb47453c22e8393f6da8800afa

Observation 0f20cc56-24ea-451f-bad4-82d678c1268b · outbound

This paper cites an unresolved cited work.

The Geometry of ReLU Networks through the ReLU Transition Graph Unresolved cited work

Reference 2604

Resolution
parse uncertain
no resolver link, observed 2026-08-15T20:55:55.207541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:55:55.207541Z digest=sha256:5872eda67c1a48f1a9e4379f4c9ea93e08b6fab0b00369d031224d891cfdc0ff

Pith citing papers

Observation a0c98cc1-eee2-4757-9ae4-bff2e0102c37 · inbound

Discrete Functional Geometry of ReLU Networks via ReLU Transition Graphs cites this paper.

Discrete Functional Geometry of ReLU Networks via ReLU Transition Graphs The Geometry of ReLU Networks through the ReLU Transition Graph

Reference 5

Resolution
verified exact
local_arxiv, observed 2026-08-15T16:37:56.702935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-15T16:37:56.522303Z digest=sha256:0eb165461b940294806e4354a2420334dfe8a98b5763c9ef41c78e902db48b36