LAKER learns a data-dependent preconditioner to reduce condition numbers by up to three orders of magnitude and accelerate convergence over twenty-fold for regularized attention kernel regression in spectrum cartography.
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2026 3verdicts
UNVERDICTED 3representative citing papers
A deep state space model on rs-fMRI time series predicts Alzheimer's behavior scores better than functional connectivity approaches and identifies key predictive brain regions.
TopoMamba improves medical image segmentation by combining topology-aware diagonal scans with standard cross-scans and a HSIC Gate for efficient fusion, yielding gains on thin and curved targets like the pancreas.
citing papers explorer
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Accelerating Regularized Attention Kernel Regression for Spectrum Cartography
LAKER learns a data-dependent preconditioner to reduce condition numbers by up to three orders of magnitude and accelerate convergence over twenty-fold for regularized attention kernel regression in spectrum cartography.
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Behavior Score Prediction in Resting-State Functional MRI by Deep State Space Modeling
A deep state space model on rs-fMRI time series predicts Alzheimer's behavior scores better than functional connectivity approaches and identifies key predictive brain regions.
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TopoMamba: Topology-Aware Scanning and Fusion for Segmenting Heterogeneous Medical Visual Media
TopoMamba improves medical image segmentation by combining topology-aware diagonal scans with standard cross-scans and a HSIC Gate for efficient fusion, yielding gains on thin and curved targets like the pancreas.