Paper Citation Record · LEDGER

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2106.01613.

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

pith.paper-citation-record.v1
2106.01613 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:13:49.374684Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T10:29:44.726205Z

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 fea17614-45b3-48a2-bd20-8cb13b8af2c8 · inbound

An Explainable Gaussian Process Auto-encoder for Tabular Data cites this paper.

An Explainable Gaussian Process Auto-encoder for Tabular Data NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T13:13:49.374684Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 16a83309-5ed3-4ab8-9505-c8dbe6622400 · inbound

When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate cites this paper.

When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-03T18:48:43.580610Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a3981096-efb7-4da4-a814-d48c3c37d796 · inbound

ParamBoost: Gradient Boosted Piecewise Cubic Polynomials cites this paper.

ParamBoost: Gradient Boosted Piecewise Cubic Polynomials NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-10T11:35:19.376799Z

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 75d53e96-3c24-4d57-9f25-e275771fa62e · inbound

Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology cites this paper.

Geometry-Aware Uncertainty Coresets for Robust Visual In-Context Learning in Histopathology NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-12T16:35:22.200575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae1f2293-1024-40bb-a79b-1e12d61e5d2e · inbound

Generalized Functional ANOVA in Closed-Form: A Unified View of Additive Explanations cites this paper.

Generalized Functional ANOVA in Closed-Form: A Unified View of Additive Explanations NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-19T23:57:53.098911Z

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 81b097e2-0c46-4337-80f4-dec213132437 · inbound

Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression cites this paper.

Solve for the Hyperparameter, Skip the Search: Kolmogorov-Optimal Scaling Laws for Spline Regression NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

Reference 52

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T10:29:44.728456Z

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.