A new corpus of 108 mixed string-numeric tables shows that advanced tabular learners with basic string embeddings perform well on most real-world data, while large LLM encoders help on free-text heavy tables.
Scikit- learn: Machine learning in python.the Journal of machine Learning research, 12:2825–2830
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MulTaBench is a new collection of 40 image-tabular and text-tabular datasets designed to test target-aware representation tuning in multimodal tabular models.
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Two methods are introduced to learn plug-in composite surrogates that maximize effect predictiveness, with the direct surrogate-effect modeling approach outperforming baselines on synthetic data with known effects and real-world experiment data.
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StableTTA improves ImageNet-1K accuracy across 71 vision models by stabilizing logit aggregation under coherent-batch inference and enabling efficient single-forward-pass adaptation.
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TeamPath: Building MultiModal Pathology Experts with Reasoning AI Copilots
TeamPath introduces a reinforcement-learning-powered multimodal AI copilot for pathology that generates reasoned diagnoses and integrates image and transcriptomic data.