Agentic-imodels evolves scikit-learn regressors via an autoresearch loop to jointly boost predictive performance and LLM-simulatability, improving downstream agentic data science tasks by up to 73% on the BLADE benchmark.
arXiv:1811.10154 [cs, stat]
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A rule-based sleep staging method operationalizing AASM scoring rules achieves 60.5% agreement with human majority-vote consensus on 50 PSG recordings while providing epoch-level explanations.
A new scale-aware diagnostic framework shows that unconstrained diffusion generative models exhibit structural freezing and instability instead of smooth physical responses under multiscale perturbations.
MedFormer-UR integrates evidential uncertainty from Dirichlet distributions and class-specific prototypes into a transformer to improve calibration and selective prediction on medical images across four modalities.
citing papers explorer
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Agentic-imodels: Evolving agentic interpretability tools via autoresearch
Agentic-imodels evolves scikit-learn regressors via an autoresearch loop to jointly boost predictive performance and LLM-simulatability, improving downstream agentic data science tasks by up to 73% on the BLADE benchmark.
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Staging by the Book: Automatic Sleep Stage Classification Using Scoring Rules
A rule-based sleep staging method operationalizing AASM scoring rules achieves 60.5% agreement with human majority-vote consensus on 50 PSG recordings while providing epoch-level explanations.
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Scale-Aware Adversarial Analysis: A Diagnostic for Generative AI in Multiscale Complex Systems
A new scale-aware diagnostic framework shows that unconstrained diffusion generative models exhibit structural freezing and instability instead of smooth physical responses under multiscale perturbations.
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MedFormer-UR: Uncertainty-Routed Transformer for Medical Image Classification
MedFormer-UR integrates evidential uncertainty from Dirichlet distributions and class-specific prototypes into a transformer to improve calibration and selective prediction on medical images across four modalities.