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Paper Citation Record · LEDGER

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions

As of 17 August 2026, this Paper Citation Record lists 42 of 42 outbound references and 0 inbound Pith citation observations for arXiv:2509.07236.

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

pith.paper-citation-record.v1
2509.07236 v1

Coverage vector

measured 42 of 42 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T22:41:54.761613Z

measured 42 of 42 standing notices

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

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

42 of 42 outbound references displayed

  • verified exact4
  • verified fuzzy17
  • unresolved20
  • parse uncertain0
  • malformed identifier0
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External citation measurements

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Outbound references

Observation f318ae6d-1d8c-492e-88fa-902104ca142b · outbound

This paper cites Widrow, M.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Widrow, M

Reference 1

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Observation a6fff925-9891-4183-8f6e-163a71825d58 · outbound

This paper cites Cauchy, M´ethode g´en´erale pour la r´esolution des syst`emes d’´equations simultan´ees, Comptes Rendus 25 (1847) 536–538.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Cauchy, M´ethode g´en´erale pour la r´esolution des syst`emes d’´equations simultan´ees, Comptes Rendus 25 (1847) 536–538

Reference 2

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Observation 7917ce59-963a-455d-941b-b92855fc47e5 · outbound

This paper cites Robbins, S.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Robbins, S

Reference 3

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Observation 60678532-0a81-4282-9599-47c990f0ab80 · outbound

This paper cites Widrow, M.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Widrow, M

Reference 4

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Observation d9acfe9c-5c5a-4697-8bab-ad9171a0cea6 · outbound

This paper cites an unresolved cited work.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Unresolved cited work

Reference 5

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Observation 3086b5d0-0a4d-4bf9-958b-6df054e7ce45 · outbound

This paper cites Pascanu, T.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Pascanu, T

Reference 6

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source=pdf_text observed=2026-08-04T22:41:54.621573Z digest=sha256:3679a2daab0c3e0afc20d0c4e02aff569a69188390b10307db2e94c5433a1deb

Observation fb41bfe1-392d-4e5d-bfe8-ad4a052eea99 · outbound

This paper cites Bottou, Stochastic gradient learning in neural networks, in: Proceedings of Neuro-N ˆımes 91, EC2, Nimes, France, 1991.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Bottou, Stochastic gradient learning in neural networks, in: Proceedings of Neuro-N ˆımes 91, EC2, Nimes, France, 1991

Reference 7

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Observation aca58a01-12d9-4820-ae54-59189830d29d · outbound

This paper cites Qian, On the momentum term in gradient descent learning algorithms, Neural Netw.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Qian, On the momentum term in gradient descent learning algorithms, Neural Netw

Reference 8

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source=pdf_text observed=2026-08-04T22:41:54.630141Z digest=sha256:29d922d957c2ee9af54f96a1f23bafbdb52e003fbe9f470d6a9925a30462d22d

Observation ba6d4e55-8502-4598-8592-2900ac43f262 · outbound

This paper cites an unresolved cited work.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Unresolved cited work

Reference 9

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source=pdf_text observed=2026-08-04T22:41:54.633987Z digest=sha256:e53dee158b19926ce1ef7cf8c7a9e54bd90bef7ba00e0a6ba4d3c6158127f927

Observation eb24705b-4d83-43e9-ab9c-729b09418231 · outbound

This paper cites Mahdavimanshadi, M.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Mahdavimanshadi, M

Reference 10

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Observation c6c37b5a-7675-4bd2-868f-0ab7b6df9dba · outbound

This paper cites Santos, T.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Santos, T

Reference 11

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source=pdf_text observed=2026-08-04T22:41:54.641554Z digest=sha256:58d7eb9aa4037f8a3dcf4bdd42598a5d0885c596b6166e2cb6f90ba78bb7d032

Observation 187e64c5-fb95-41e7-aaa4-de9f07357174 · outbound

This paper cites an unresolved cited work.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Unresolved cited work

Reference 12

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Observation da7585e2-1345-4584-bee6-dd6d3495c667 · outbound

This paper cites an unresolved cited work.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Unresolved cited work

Reference 13

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Observation 0ebc22fb-4210-4651-a206-6bb473820f0f · outbound

This paper cites Martens, R.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Martens, R

Reference 14

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Observation d8d19a26-cf42-46d7-b621-5c610da9d31e · outbound

This paper cites Ebadi, A.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Ebadi, A

Reference 15

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source=pdf_text observed=2026-08-04T22:41:54.656217Z digest=sha256:cb2d5c050cda358d93ad9ef15ea897a14caa99f48a738e24c77eaf814bf7bb93

Observation e3df1ca4-3824-4d77-9ee7-fbd4a6079272 · outbound

This paper cites Adam: A Method for Stochastic Optimization.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Adam: A Method for Stochastic Optimization

Reference 16

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source=pdf_text observed=2026-08-04T22:41:54.659796Z digest=sha256:d8be67da84999962174c5d7ed03f4737fdad5f4f74e8171f370e18187a132607

Observation b1fe86b1-748a-48d2-99c4-fd3aaaff8bb1 · outbound

This paper cites Duchi, E.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Duchi, E

Reference 17

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source=pdf_text observed=2026-08-04T22:41:54.663871Z digest=sha256:744d7101681acb09daee0c4089d10ea80a5da0fb76a760b8b952e66e02cd5044

Observation 2ecb06d9-eeab-43fd-9419-57b2ab5d1497 · outbound

This paper cites Tieleman, G.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Tieleman, G

Reference 18

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

source=pdf_text observed=2026-08-04T22:41:54.667329Z digest=sha256:4816ca91d57ee9685ebcb90fc5fcbb47059b129eadb492fc5a94e6eb18efa520

Observation 722ecbcf-3e80-46e3-84c9-e55213d1c77c · outbound

This paper cites Nesterov, A method for solving the convex programming problem with convergence rateo(1/k2), Doklady Akademii Nauk SSSR 269 (3) (1983) 543–547.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Nesterov, A method for solving the convex programming problem with convergence rateo(1/k2), Doklady Akademii Nauk SSSR 269 (3) (1983) 543–547

Reference 19

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source=pdf_text observed=2026-08-04T22:41:54.670714Z digest=sha256:b2482805a9dc2058f022e6634774073fe02f754e1af3c01d5911ab43e74ceee0

Observation a3bba823-5e49-49a7-b0c3-540077481be1 · outbound

This paper cites ADADELTA: An Adaptive Learning Rate Method.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions ADADELTA: An Adaptive Learning Rate Method

Reference 20

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Observation 4919d3d5-bffc-4311-a18d-1fe51eb6ef87 · outbound

This paper cites an unresolved cited work.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Unresolved cited work

Reference 21

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Observation 185627e8-6e18-4c78-86c5-3aea45c443e9 · outbound

This paper cites an unresolved cited work.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Unresolved cited work

Reference 22

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source=pdf_text observed=2026-08-04T22:41:54.684266Z digest=sha256:2e68ff6e19202862dc0280116dd786374264c4de6d78169143cd3a30b9c195bd

Observation ad8f8516-20c8-4289-a0bd-df23128b6690 · outbound

This paper cites Gradient Centralization: A New Optimization Technique for Deep Neural Networks.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Gradient Centralization: A New Optimization Technique for Deep Neural Networks

Reference 23

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local_arxiv, observed 2026-08-04T22:41:54.995964Z

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Observation 99e77d9f-bf42-4240-93d2-35a1d60074f0 · outbound

This paper cites Salimans, D.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Salimans, D

Reference 24

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Observation b4242110-55b1-42c2-913c-eecc532f887d · outbound

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

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Reference 25

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Observation 2b910e41-f09f-429c-9f93-7dc1aca7103d · outbound

This paper cites Santurkar, D.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Santurkar, D

Reference 26

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Observation 51d5871e-6242-48de-8fa2-0967c9074dce · outbound

This paper cites Ulyanov, A.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Ulyanov, A

Reference 27

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source=pdf_text observed=2026-08-04T22:41:54.703332Z digest=sha256:ccf91b788073c83af065d29ba52f6b513b06ec7abd02c43d46e5eee489d628e3

Observation 630b2bb0-dd18-4f6d-b641-cd213be2dd57 · outbound

This paper cites Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

Reference 28

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source=pdf_text observed=2026-08-04T22:41:54.707450Z digest=sha256:7c5bb91c04a6f34840f116de0a6493ea4f96400c2cd790516dc0149929b24041

Observation d0a99185-03ac-4d65-8099-9e7a30c5bab8 · outbound

This paper cites Layer Normalization.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Layer Normalization

Reference 29

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source=pdf_text observed=2026-08-04T22:41:54.711333Z digest=sha256:d0cb212802596e270bfbb4617a33f2aa3ebc9fb0d66dd650bbf8ad9f17f0625f

Observation 664a8b7a-6330-4fc4-8790-8d77fcf88ff2 · outbound

This paper cites Group Normalization.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Group Normalization

Reference 30

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source=pdf_text observed=2026-08-04T22:41:54.715196Z digest=sha256:81c38b4e57741887f8201dda66eede6c666707f08a853b5d97e66f499b8b355e

Observation 7a2e23b2-f1fa-4c3f-b59e-b737b9c542aa · outbound

This paper cites Micro-Batch Training with Batch-Channel Normalization and Weight Standardization.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Micro-Batch Training with Batch-Channel Normalization and Weight Standardization

Reference 31

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source=pdf_text observed=2026-08-04T22:41:54.719204Z digest=sha256:8f282356474d9f478d126a6f0c8a8e893ffae30d967545c6987aabf243535849

Observation c7a8f869-22ec-4dc9-bcfe-c81f2e39c15e · outbound

This paper cites Huang, X.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Huang, X

Reference 32

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doi, observed 2026-08-04T22:41:54.805964Z

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

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Observation 05e330b2-e541-4663-b946-8480f3e1f285 · outbound

This paper cites Sutskever, J.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Sutskever, J

Reference 33

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raw_fallback, observed 2026-08-04T22:41:55.157033Z

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

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Observation 3caa6e44-4d28-48d2-8da7-4384f3722c4a · outbound

This paper cites Pascanu, T.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Pascanu, T

Reference 34

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

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Observation 3644ef98-811a-4db4-91ad-c1cddd97e568 · outbound

This paper cites an unresolved cited work.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Unresolved cited work

Reference 35

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

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Observation 0bc66640-2799-4ddf-9bf0-8a92edc44a3e · outbound

This paper cites Adding Gradient Noise Improves Learning for Very Deep Networks.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Adding Gradient Noise Improves Learning for Very Deep Networks

Reference 36

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source=pdf_text observed=2026-08-04T22:41:54.738515Z digest=sha256:1997ee612f6644f31442f9acfb552ac5c9bbd199eab86a462a6f140bb2de984a

Observation e58433c8-8dce-4301-b566-04a154f32266 · outbound

This paper cites Alpaydin, C.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Alpaydin, C

Reference 37

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unresolved
no resolver link, observed 2026-08-04T22:41:54.742485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:41:54.742485Z digest=sha256:42531e1a82071d6aedea0039f94b937ab2af961f44bccb7c40b08d931541aa5b

Observation 47867ea4-ce32-4511-8229-4339443b9712 · outbound

This paper cites LeCun, C.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions LeCun, C

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:41:55.117301Z

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=pdf_text observed=2026-08-04T22:41:54.746179Z digest=sha256:8cf03f67b5dee009ed17d93ecbaa0dad3ab0872a936d5a79fd41dd08e5cd951c

Observation 2f100049-0dec-44b1-830f-01272b0c1982 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 39

Resolution
unresolved
no resolver link, observed 2026-08-04T22:41:54.749813Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:41:54.749813Z digest=sha256:602e0ae9cd45aab00b50cda174928142382c8bf127b8ae078c6960519be33e64

Observation 70123dd2-7ac8-4372-aff6-bd7c2e309c9e · outbound

This paper cites Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation

Reference 40

Resolution
unresolved
no resolver link, observed 2026-08-04T22:41:54.753829Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T22:41:54.753829Z digest=sha256:349f5a1ebf97793e774a08444c766f10eda848f59d5ebeb2c9b447a43a656299

Observation 605bbba2-74dc-485e-bcd3-0ab89bbc93e8 · outbound

This paper cites Courbariaux, Y.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Courbariaux, Y

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:41:55.104536Z

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=pdf_text observed=2026-08-04T22:41:54.757825Z digest=sha256:4dc18c93f66d242aec891fcab278ac17b76738b4c47f362f55733e10a3be54e4

Observation ef315306-63c6-45a1-838d-1bd7936f4ccb · outbound

This paper cites Rastegari, V.

Breaking the Conventional Forward-Backward Tie in Neural Networks: Activation Functions Rastegari, V

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-04T22:41:55.091473Z

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=pdf_text observed=2026-08-04T22:41:54.761613Z digest=sha256:ec7781d8ade5f205b79bf8d0923225adf81b95ed0e258ab0febbda24281cc05e

Pith citing papers

No inbound Pith citation observations are available.