Derives optimal low-rank subspace for Laplace approx in BNNs, provides scalable outperforming version, and new comparison metric.
Being bayesian, even just a bit, fixes overconfidence in relu networks,
2 Pith papers cite this work. Polarity classification is still indexing.
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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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Low Rank Based Subspace Inference for the Laplace Approximation of Bayesian Neural Networks
Derives optimal low-rank subspace for Laplace approx in BNNs, provides scalable outperforming version, and new comparison metric.
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A 65 nm Multi-Modal Bayesian Inference Engine with 16.3 fJ/Sample Calibration-Free GRNG for Risk-Aware At-Home Skin Lesion Screening
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.