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3,367 papers
SOMTab, a Set-Order Mamba architecture for efficient tabular in-context learning, and DCH-TailMix, a synthetic prior that combines degree-corrected graph heterogeneity with mixed heavy-tailed regimes to diversify synthetic dependency structures are introduced.
Clinical modeling experience is established as a promising collaborative object beyond conventional parameter-centric FL and point toward federated autonomous clinical intelligence.
Actionable case-based feature importance (A-CBFI), a diagnosis-prescription integrated framework for tabular machine learning, provides targeted and actionable recourse while maintaining causal validity and achieving full relative convergence across all causally feasible instances.
Measuring tactical accuracy and playing strength together across a matched-compute sweep, the two dissociate and a control fine-tuned on puzzles alone posts the study's largest tactical gains while shedding roughly 260 Elo; a better puzzle-solver is not thereby a stronger player.
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RiskBlend is proposed, a classifier-agnostic prioritization framework that combines four complementary risk signals: historical failure patterns, prediction shift, decision-boundary shift, and neighborhood change that achieves the highest average APFD in all 80 dataset-classifier-scenario combinations.
The developed CARDINAL (Cardiovascular Assessment via Representation learning from Deep Imaging with Nested Anatomical Latent embeddings), a clinically grounded framework that learns compact representations from routine non-contrast cardiac CT for major adverse cardiovascular event (MACE) prediction, suggests that non-contrast cardiac CT contains prognostic information beyond conventional risk equations, CAC scoring, and engineered imaging biomarkers.
Curvature-Aware Radius Shrinkage for Adaptive Nearest Neighbor Classification (CARSANN) is introduced, a geometry-driven framework that adapts the spatial support of each neighborhood according to local geometric complexity and is competitive with adaptive nearest-neighbor methods.
An important finding is that even after full RANSAC orthorectification, bounding box models overestimate pothole depth by 0.16 to 0.21 cm compared to pixel precise segmentation masks, which confirms that the pavement inclusion bias is structural rather than a calibration artifact.
This work presents Eager Multi-Resolution HyperNEAT (EMR-HyperNEAT), which reformulates adaptive substrate discovery as a batch tensor operation: shared position grids are precomputed for all depths, every position is evaluated in one vectorized CPPN call, then the same variance criterion filters the output.
Experiments and comparisons show that SEMGNN achieves competitive or improved predictive performance while providing more faithful and compact label-conditioned explanations, compared to post-hoc methods.
The results demonstrate that selective, impact-weighted augmentation is vital for balancing practical robustness with the preservation of subtle diagnostic features, and demonstrate that models trained on "destroyed" patches consistently outperform baselines on independent real-world datasets.
It is demonstrated that source-precision auditing alone does not rule out quantization-triggered behavior and that the final deployed configuration must be included in behavioral certification for trustworthy edge AI.
Machine Intelligence
A weeklong summer workshop brought higher education faculty to campus to explore how AI and machine learning materials can be adapted for their classrooms.
General Science
Climate & Sustainability
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
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