Introduces architecture distributions for stochastic segmentation by sampling discrete architectures from a learned distribution, trained via set-level IoU-based supervision and evolutionary candidate bank construction, claiming SOTA on LIDC-IDRI.
Modeling multimodal aleatoric uncertainty in segmentation with mixture of stochastic experts
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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An information-theoretic optimization framework for task-adapted CS-MRI enables adaptive sampling at arbitrary ratios and probabilistic inference for uncertainty while supporting joint reconstruction-task or privacy-focused scenarios.
citing papers explorer
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Neural Architecture Distributions: A New Paradigm for Stochastic Segmentation
Introduces architecture distributions for stochastic segmentation by sampling discrete architectures from a learned distribution, trained via set-level IoU-based supervision and evolutionary candidate bank construction, claiming SOTA on LIDC-IDRI.
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Information-Theoretic Optimization for Task-Adapted Compressed Sensing Magnetic Resonance Imaging
An information-theoretic optimization framework for task-adapted CS-MRI enables adaptive sampling at arbitrary ratios and probabilistic inference for uncertainty while supporting joint reconstruction-task or privacy-focused scenarios.