Introduces dimension-disentangled influence estimation to prune or reweight training samples for MVRMs, outperforming global scalar filtering in alignment with ground truth.
arXiv preprint arXiv:2404.11599 , year=
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Wahkon unifies Kolmogorov superposition with RKHS regularization to produce a deep network whose penalized estimator is exactly the MAP under a hierarchical GP prior and achieves minimax-optimal rates.
SSLA approximates the posterior predictive distribution by refitting Bayesian models on self-predicted data, yielding a deterministic uncertainty measure that outperforms standard Laplace approximations in calibration on regression tasks.
A 65 nm compute-in-memory chip implements multi-modal Bayesian neural networks with a calibration-free GRNG to deliver risk-aware skin lesion screening with reported gains in coverage, robustness, and efficiency over unimodal baselines.
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Self-Supervised Laplace Approximation for Bayesian Uncertainty Quantification
SSLA approximates the posterior predictive distribution by refitting Bayesian models on self-predicted data, yielding a deterministic uncertainty measure that outperforms standard Laplace approximations in calibration on regression tasks.