Paper Citation Record · LEDGER

Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2412.16243.

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

pith.paper-citation-record.v1
2412.16243 v1

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 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:06:13.722238Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T07:04:21.532153Z

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 b6e713f0-8d1b-4438-a016-38e5aa82f808 · inbound

MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning cites this paper.

MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-15T20:11:34.282727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 6399dd01-cfb0-4982-9ea6-b316869f8382 · inbound

VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning cites this paper.

VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-12T07:56:27.032029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 29358c85-fa39-46ff-bc69-c403ceca74f2 · inbound

VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning cites this paper.

VT-Bench: A Unified Benchmark for Visual-Tabular Multi-Modal Learning Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-21T00:33:52.793869Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 00bc0266-ace6-4ef3-9f9a-0f193817ce35 · inbound

MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image cites this paper.

MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data

Reference 98

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T05:51:24.761694Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4b6447a8-0f1d-4304-b2c6-a18ccbe7be48 · inbound

Beyond IID: How General Are Tabular Foundation Models, Really? cites this paper.

Beyond IID: How General Are Tabular Foundation Models, Really? Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data

Reference 117

Resolution: verified exact; arxiv_id, observed 2026-06-30T07:04:21.534599Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 21d84596-18cd-4ae8-a670-159a501a9e44 · inbound

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning cites this paper.

Walkable to Whom? Capturing Subjective Variability in Walkability Perception Using Multimodal Deep Learning Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T18:06:13.722238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.