Paper Citation Record · LEDGER

Learning Activation Functions: A new paradigm for understanding Neural Networks

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:1906.09529.

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

pith.paper-citation-record.v1
1906.09529 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 2026-08-07T06:34:17.273281+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:17:25.775083Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T19:43:54.877465Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
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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 8d986e25-d3d5-4b1b-ac71-aeb05a309378 · inbound

KAN: Kolmogorov-Arnold Networks cites this paper.

KAN: Kolmogorov-Arnold Networks Learning Activation Functions: A new paradigm for understanding Neural Networks

Reference 88

Resolution: verified exact; arxiv_id, observed 2026-05-11T23:42:05.290378Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 99352312-3096-40f6-8df6-bd8ba47401fc · inbound

Towards Identifiability of Interventional Stochastic Differential Equations cites this paper.

Towards Identifiability of Interventional Stochastic Differential Equations Learning Activation Functions: A new paradigm for understanding Neural Networks

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T15:17:25.775083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59d846d2-476e-42b3-8f6a-20effc699cef · inbound

Tangma: A Tanh-Guided Activation Function with Learnable Parameters cites this paper.

Tangma: A Tanh-Guided Activation Function with Learnable Parameters Learning Activation Functions: A new paradigm for understanding Neural Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T20:42:00.993958Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db4b07f4-f9ab-49ae-95c5-c48142809276 · inbound

A parametric activation function based on Wendland RBF cites this paper.

A parametric activation function based on Wendland RBF Learning Activation Functions: A new paradigm for understanding Neural Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T22:00:11.753996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3baa3cdd-fd01-4139-b35c-33f6ffaae3c3 · inbound

Hybrid Ensemble Approaches: Optimal Deep Feature Fusion and Hyperparameter-Tuned Classifier Ensembling for Enhanced Brain Tumor Classification cites this paper.

Hybrid Ensemble Approaches: Optimal Deep Feature Fusion and Hyperparameter-Tuned Classifier Ensembling for Enhanced Brain Tumor Classification Learning Activation Functions: A new paradigm for understanding Neural Networks

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T16:55:33.061391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31bc019f-874f-4f38-92ba-b3084291b05a · inbound

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

FlexAct: Why Learn when you can Pick? Learning Activation Functions: A new paradigm for understanding Neural Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T11:27:30.167054Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 31f708f9-e28f-4a63-b1ec-30fe09520f3b · inbound

Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches cites this paper.

Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches Learning Activation Functions: A new paradigm for understanding Neural Networks

Reference 135

Resolution: verified exact; arxiv_id, observed 2026-05-10T09:23:37.366573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 4cefd46b-2bf6-4066-8678-9cd6576f4914 · inbound

More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations cites this paper.

More Expressive Feedforward Layers: Part I. Token-Adaptive Mixing of Activations Learning Activation Functions: A new paradigm for understanding Neural Networks

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-06-29T19:43:54.879565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 0ffe3125-2a65-4514-ab4b-f85bb9dd1b5f · inbound

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks cites this paper.

Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks Learning Activation Functions: A new paradigm for understanding Neural Networks

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-01T17:31:46.698367Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.