Tabular foundation models excel on tiny- to medium-sized IID data but are outperformed by traditional tree-based and deep learning models on non-IID, large, and high-dimensional datasets, based on evaluations across 11 models and 142 datasets in the new BeyondArena benchmark.
Mantis: Lightweight calibrated foundation model for user-friendly time series classification
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MoRA is a new retrieval-augmented module for IMU-based human activity recognition that uses uncertainty-adaptive fusion of retrieved motion patterns to improve model performance.
COMODO is a cross-modal self-supervised distillation framework that uses a frozen video encoder and dynamic instance queue to align video and IMU embeddings, improving IMU-based egocentric HAR to match supervised performance.
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Modular Retrieval-Augmented Generalization for Human Action Recognition
MoRA is a new retrieval-augmented module for IMU-based human activity recognition that uses uncertainty-adaptive fusion of retrieved motion patterns to improve model performance.