Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning Activation Functions to Improve Deep Neural Networks
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:1412.6830.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T05:11:33.768564Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-06-29T19:43:54.893136Z
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 c43e2a29-ece4-4f24-82f6-e9695deb1d06 · inbound
Searching for Activation Functions cites this paper.
Searching for Activation Functions Learning Activation Functions to Improve Deep Neural Networks
Reference 1
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T02:50:54.062702Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4e3ede48-5ac3-4640-b87d-5ed6840dc058 · inbound
Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning cites this paper.
Graph Interpolating Activation Improves Both Natural and Robust Accuracies in Data-Efficient Deep Learning Learning Activation Functions to Improve Deep Neural Networks
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-05-24T21:14:56.971366Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 4108d3a9-c257-4ea0-8d83-1da43a6a6a50 · inbound
From KAN to GR-KAN: Advancing Speech Enhancement with KAN-Based Methodology cites this paper.
From KAN to GR-KAN: Advancing Speech Enhancement with KAN-Based Methodology Learning Activation Functions to Improve Deep Neural Networks
Reference 40
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T05:11:33.768564Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f8012c82-7e0a-4186-b24a-9512c31fda49 · inbound
Hybrid activation functions for deep neural networks: S3 and S4 -- a novel approach to gradient flow optimization cites this paper.
Hybrid activation functions for deep neural networks: S3 and S4 -- a novel approach to gradient flow optimization Learning Activation Functions to Improve Deep Neural Networks
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T12:39:07.140374Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dcc94256-a9ce-40a3-aae6-e1b18504774b · inbound
Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks cites this paper.
Quantum Variational Activation Functions Empower Kolmogorov-Arnold Networks Learning Activation Functions to Improve Deep Neural Networks
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T16:32:36.272736Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8cb51383-12ac-4afa-ad9e-9c064b33134c · inbound
Competing nonlinearities, criticality, and order-to-chaos transition in deep networks cites this paper.
Competing nonlinearities, criticality, and order-to-chaos transition in deep networks Learning Activation Functions to Improve Deep Neural Networks
Reference 43
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T18:26:12.166464Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation cb7038cf-8ac1-45be-881b-e8d3017754ab · 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 to Improve Deep Neural Networks
Reference 1
- Resolution
- verified exact
- local_arxiv, observed 2026-06-29T19:43:54.894489Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.
Observation 5b5436f0-8d91-4eb7-b691-8803cce259d2 · inbound
Rethinking Neural Nonlinearity as Gating cites this paper.
Rethinking Neural Nonlinearity as Gating Learning Activation Functions to Improve Deep Neural Networks
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T04:33:35.163697Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 5fed4fd8-741e-4f13-b2c1-bf72312a9a29 · inbound
GNet: A scalable and flexible Gaussian process network with nonparametric neurons cites this paper.
GNet: A scalable and flexible Gaussian process network with nonparametric neurons Learning Activation Functions to Improve Deep Neural Networks
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-07-14T09:38:11.946002Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 10cf294f-b7c0-4f0b-bc66-b34ec503a601 · inbound
GNet: A scalable and flexible Gaussian process network with nonparametric neurons cites this paper.
GNet: A scalable and flexible Gaussian process network with nonparametric neurons Learning Activation Functions to Improve Deep Neural Networks
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T07:15:24.988851Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.