arXiv (All)
open access
Architecture Alignment With Sparse Priors in Tabular Foundation Models
Tabular foundation models (TFMs) are increasingly popular because they deliver strong predictions on new datasets through in-context learning, without task-specific training or extensive tuning. Yet released TFMs differ simultaneously in their pretraining priors, architectures, and objectives, obscu
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Training Fair Tabular Foundation Models#458298
- Paton-Native AI Architectures: Admissibility-Driven Learning Systems within the Paton Framework#73396
- Benchmarking Tabular Foundation Models as Surrogates in Expensive Evolutionary Optimization#1011826
- Causilo Technical Report#1037549
- Ensembling Tabular Foundation Models - A Diversity Ceiling And A Calibration Trap#200481
Record · ID 1132084
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive