Paper Citation Record · LEDGER

(GG) MoE vs. MLP on Tabular Data

As of 13 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 1 inbound Pith citation observation for arXiv:2502.03608.

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

pith.paper-citation-record.v1
2502.03608 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T04:26:41.700916Z

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:19:59.550570Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T19:20:00.490221Z

Reference resolution

13 of 13 outbound references displayed

verified exact1
verified fuzzy0
unresolved12
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c42a26c8-8f35-42c8-886b-6cf2402339d9 · outbound

This paper cites TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling.

(GG) MoE vs. MLP on Tabular Data TabM: Advancing Tabular Deep Learning with Parameter-Efficient Ensembling

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-09T04:26:41.668617Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed8d76e4-0d54-4b70-a42e-349aa54f0f42 · outbound

This paper cites an unresolved cited work.

(GG) MoE vs. MLP on Tabular Data Unresolved cited work

Reference 9

Resolution: unresolved; raw_fallback, observed 2026-08-09T04:26:41.821944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation ffd54ced-cfe1-489c-8b3b-29403680437e · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

(GG) MoE vs. MLP on Tabular Data Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-09T04:26:41.691547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ed0bb05-a327-4f09-a488-55ea5057aae9 · outbound

This paper cites SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training.

(GG) MoE vs. MLP on Tabular Data SAINT: Improved Neural Networks for Tabular Data via Row Attention and Contrastive Pre-Training

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-09T04:26:41.694395Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f3237f8f-7636-4f1f-858d-d8115f9316d7 · outbound

This paper cites BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning.

(GG) MoE vs. MLP on Tabular Data BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-09T04:26:41.697620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 832748ed-dbb5-4190-9185-3dafe291effc · outbound

This paper cites TabTransformer: Tabular Data Modeling Using Contextual Embeddings.

(GG) MoE vs. MLP on Tabular Data TabTransformer: Tabular Data Modeling Using Contextual Embeddings

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-09T04:26:41.675514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d23e68bc-3257-4795-b656-6fae4a4b32d9 · outbound

This paper cites TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization.

(GG) MoE vs. MLP on Tabular Data TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization

Reference 2016

Resolution: verified exact; local_arxiv, observed 2026-08-09T04:26:41.769208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation dfd34694-d491-4fe7-b6f9-8eba664e303f · outbound

This paper cites Decoupled Weight Decay Regularization.

(GG) MoE vs. MLP on Tabular Data Decoupled Weight Decay Regularization

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-09T04:26:41.685480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89f4c747-4b63-4fa8-8b5c-dd63fc5ed48f · outbound

This paper cites From Sparse to Soft Mixtures of Experts.

(GG) MoE vs. MLP on Tabular Data From Sparse to Soft Mixtures of Experts

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-09T04:26:41.688700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46c6207b-cccb-4966-a863-414a2421f800 · outbound

This paper cites SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption.

(GG) MoE vs. MLP on Tabular Data SCARF: Self-Supervised Contrastive Learning using Random Feature Corruption

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-09T04:26:41.661175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cda1fc54-d834-4cfd-9e49-28645459fa3b · outbound

This paper cites Categorical Reparameterization with Gumbel-Softmax.

(GG) MoE vs. MLP on Tabular Data Categorical Reparameterization with Gumbel-Softmax

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-09T04:26:41.678984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 336d67be-f157-4280-9dd3-2ab0c99b997b · outbound

This paper cites Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data.

(GG) MoE vs. MLP on Tabular Data Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-09T04:26:41.672006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b684e82b-4a9c-4bc9-afad-96d4871aec55 · outbound

This paper cites A Review of Sparse Expert Models in Deep Learning.

(GG) MoE vs. MLP on Tabular Data A Review of Sparse Expert Models in Deep Learning

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-09T04:26:41.665208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation b7972f63-07a1-41ba-b5c9-0b46bd4b8db2 · inbound

Universal Embeddings of Tabular Data cites this paper.

Universal Embeddings of Tabular Data (GG) MoE vs. MLP on Tabular Data

Reference 5

Resolution: verified exact; local_arxiv, observed 2026-08-06T19:20:00.494231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.