Schema-1 is the first Data Language Model that natively understands raw tabular data and outperforms gradient-boosted ensembles, AutoML, and prior tabular foundation models on row-level prediction and imputation tasks.
arXiv:2405.01147 [cs]
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TFM-Retouche is an architecture-agnostic input-space residual adapter that improves tabular foundation model accuracy on 51 datasets by learning input corrections through the frozen backbone, with an identity guard to fall back to the original model.
TabGRAA enables self-improving tabular language models through iterative group-relative advantage alignment using modular automated quality signals like distinguishability classifiers.
SQuARE is a hybrid retrieval system that uses a complexity score to route tabular queries between chunk-based and SQL-based paths, outperforming single-strategy baselines and GPT-4o on precision and accuracy for complex spreadsheets.
TREASURE is a transformer model for payment transactions that boosts abnormal behavior detection performance by 111% over production systems and improves recommendation models by 104% when used as an embedding provider.
TabPFN maintains high ROC-AUC and structured attention under controlled additions of irrelevant features, nonlinear correlations, and mislabeled targets in binary classification.
citing papers explorer
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Data Language Models: A New Foundation Model Class for Tabular Data
Schema-1 is the first Data Language Model that natively understands raw tabular data and outperforms gradient-boosted ensembles, AutoML, and prior tabular foundation models on row-level prediction and imputation tasks.
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TFM-Retouche: A Lightweight Input-Space Adapter for Tabular Foundation Models
TFM-Retouche is an architecture-agnostic input-space residual adapter that improves tabular foundation model accuracy on 51 datasets by learning input corrections through the frozen backbone, with an identity guard to fall back to the original model.
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Self-Improving Tabular Language Models via Iterative Group Alignment
TabGRAA enables self-improving tabular language models through iterative group-relative advantage alignment using modular automated quality signals like distinguishability classifiers.
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SQuARE: Structured Query & Adaptive Retrieval Engine For Tabular Formats
SQuARE is a hybrid retrieval system that uses a complexity score to route tabular queries between chunk-based and SQL-based paths, outperforming single-strategy baselines and GPT-4o on precision and accuracy for complex spreadsheets.
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TREASURE: The Visa Payment Foundation Model for High-Volume Transaction Understanding
TREASURE is a transformer model for payment transactions that boosts abnormal behavior detection performance by 111% over production systems and improves recommendation models by 104% when used as an embedding provider.
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Noise Immunity in In-Context Tabular Learning: An Empirical Robustness Analysis of TabPFN's Attention Mechanisms
TabPFN maintains high ROC-AUC and structured attention under controlled additions of irrelevant features, nonlinear correlations, and mislabeled targets in binary classification.