TabPFN-MT is a multitask in-context learner for tabular data that sets a new state-of-the-art on deep multitask learning for datasets under 1000 samples while reducing inference cost from O(T) to O(1) passes.
TabNet: Attentive Interpretable Tabular Learning.,
6 Pith papers cite this work. Polarity classification is still indexing.
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Individually calibrated predictors become collectively miscalibrated under Brier-optimal strategic responses with positive belief correlations, but VCG aggregation restores dominant-strategy incentive compatibility and near-optimal performance.
Tabular foundation models outperform standard methods in credit risk PD and LGD tasks, with larger gains on smaller datasets when used out-of-the-box.
Physics-informed GNNs with four detector-aware graph constructions and a custom message passing layer achieve MAE 0.8525 for pT estimation on CMS trigger data with over 55% fewer parameters than baselines.
FCNN maps tabular features to fuzzy memberships, arranges them as images, and uses CNNs to classify, reporting competitive or superior results versus DT, SVM, FNN, Bayes, and RF on six generated noisy datasets.
Reveal-to-Revise integrates cross-modal attention fusion, Grad-CAM++ attribution, and bias feedback in a conditional attention WGAN-GP to report high accuracy, F1, and fairness metrics on multimodal MNIST variants and toxic text tasks.
citing papers explorer
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TabPFN-MT: A Natively Multitask In-Context Learner for Tabular Data
TabPFN-MT is a multitask in-context learner for tabular data that sets a new state-of-the-art on deep multitask learning for datasets under 1000 samples while reducing inference cost from O(T) to O(1) passes.
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When Individually Calibrated Models Become Collectively Miscalibrated
Individually calibrated predictors become collectively miscalibrated under Brier-optimal strategic responses with positive belief correlations, but VCG aggregation restores dominant-strategy incentive compatibility and near-optimal performance.
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Foundation Models for Credit Risk Prediction: A Game Changer?
Tabular foundation models outperform standard methods in credit risk PD and LGD tasks, with larger gains on smaller datasets when used out-of-the-box.
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Physics-Informed Graph Neural Networks for Transverse Momentum Estimation in CMS Trigger Systems
Physics-informed GNNs with four detector-aware graph constructions and a custom message passing layer achieve MAE 0.8525 for pT estimation on CMS trigger data with over 55% fewer parameters than baselines.
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Fuzzy Convolution Neural Networks for Tabular Data Classification
FCNN maps tabular features to fuzzy memberships, arranges them as images, and uses CNNs to classify, reporting competitive or superior results versus DT, SVM, FNN, Bayes, and RF on six generated noisy datasets.
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Reveal-to-Revise: Explainable Bias-Aware Generative Modeling with Multimodal Attention
Reveal-to-Revise integrates cross-modal attention fusion, Grad-CAM++ attribution, and bias feedback in a conditional attention WGAN-GP to report high accuracy, F1, and fairness metrics on multimodal MNIST variants and toxic text tasks.