Paper Citation Record · LEDGER

Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2109.14545.

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

pith.paper-citation-record.v1
2109.14545 v3

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 2026-08-18T06:34:40.430872+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:56:04.483696Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

17
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d413411f-fa13-4dc6-9d46-d52626a67c13 · inbound

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively cites this paper.

RedTest: Towards Measuring Redundancy in Deep Neural Networks Effectively Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T19:56:02.420302Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48c8d32f-7867-4ca9-9e95-242ec330554d · inbound

Performance Optimization of Ratings-Based Reinforcement Learning cites this paper.

Performance Optimization of Ratings-Based Reinforcement Learning Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-10T20:40:11.494329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 394cc07f-b509-4f1e-b9c9-1a9ccd3710bf · inbound

Electroweak diboson production in association with a high-mass dijet system in semileptonic final states from $pp$ collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector cites this paper.

Electroweak diboson production in association with a high-mass dijet system in semileptonic final states from $pp$ collisions at $\sqrt{s} = 13$ TeV with the ATLAS detector Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark

Reference 103

Resolution: verified exact; arxiv_id, observed 2026-05-22T22:32:12.737916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 318f28b2-1569-49f9-a08a-05cf6e56c972 · inbound

Extract the Best, Discard the Rest: CSI Feedback with Offline Large AI Models cites this paper.

Extract the Best, Discard the Rest: CSI Feedback with Offline Large AI Models Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-15T21:56:04.483696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 02ae8122-ae7d-4c83-928e-aecd1ce210dc · inbound

Criticality analysis of nuclear binding energy neural networks cites this paper.

Criticality analysis of nuclear binding energy neural networks Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark

Reference 41

Resolution: unresolved; no resolver link, observed 2026-08-06T06:01:11.669453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11598f0e-73d5-489f-8651-6ec573ea5d77 · inbound

Physics-informed neural network (PINN) modeling of charged particle multiplicity using the two-component framework in heavy-ion collisions: A comparison with data-driven neural networks cites this paper.

Physics-informed neural network (PINN) modeling of charged particle multiplicity using the two-component framework in heavy-ion collisions: A comparison with data-driven neural networks Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-18T00:15:31.702810Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0c71c84e-2469-4248-9e84-b1dc36c0dcc3 · inbound

Enhancing Reinforcement Learning in 3D Environments through Semantic Segmentation: A Case Study in ViZDoom cites this paper.

Enhancing Reinforcement Learning in 3D Environments through Semantic Segmentation: A Case Study in ViZDoom Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-03T22:43:26.947162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40cf83a4-dcc1-4add-af58-ce5831ba0ba9 · inbound

FlexAct: Why Learn when you can Pick? cites this paper.

FlexAct: Why Learn when you can Pick? Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T11:27:30.163142Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23a21835-bb86-4b42-99e2-744b6f5e946f · inbound

Solving forward and inverse wave scattering via boundary integral equations and deep learning. Applications to cloaking design cites this paper.

Solving forward and inverse wave scattering via boundary integral equations and deep learning. Applications to cloaking design Activation Functions in Deep Learning: A Comprehensive Survey and Benchmark

Reference 25

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:16:12.662778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.