arXiv (All)
open access
Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping
While deep learning models for tabular data have recently received significant research attention, the choice of objective functions for regression tasks remains an understudied degree of freedom. Several lines of research offer alternatives to the standard Mean Squared Error (MSE), but differences
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
- Revisiting Tabular Deep Regression: Objectives and the Limits of Global Early Stopping#1291788
- Deep learning of longitudinal visual fields predicts glaucoma progression rate and identifies fast progressors#1077185
- Econometrics with Pre-Trained Embeddings for Unstructured Data#1105111
- Solving In-Table Prediction Problems by Deep Neural Networks with Performance Evaluation Using Synthetic Data#821216
- TabPack: Efficient Hyperparameter Ensembles for Tabular Deep Learning#343461
Record · ID 1298405
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