Deep Ensembles
PulseAugur coverage of Deep Ensembles — every cluster mentioning Deep Ensembles across labs, papers, and developer communities, ranked by signal.
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Deep Ensembles Show Linear Mode Connectivity Under Data Shifts
Researchers have investigated the phenomenon of linear mode connectivity (LMC) in deep learning, particularly how it is affected by data shifts in ensembles of image classifiers. The study suggests that data shifts can …
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Deep learning models evaluated for machinery fault diagnosis with uncertainty
A new research paper published on arXiv explores the effectiveness of various deep learning models in diagnosing faults in rotating machinery, specifically focusing on their ability to handle uncertainty. The study comp…
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Conformal prediction offers new uncertainty guarantees for physics simulations
Researchers have introduced a novel application of split conformal prediction to neural operator-based physics simulations, offering distribution-free prediction intervals with formal coverage guarantees. This method, a…
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Deep ensembles fail to capture uncertainty in graph neural networks
A new research paper questions the effectiveness of deep ensembles for uncertainty quantification in graph neural networks. The study found that ensembles offer minimal improvement over single models, with gains primari…
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New framework unifies uncertainty-aware explainable AI
Researchers have introduced a new framework for explainable AI (XAI) that incorporates uncertainty awareness, moving beyond deterministic attribution maps. This approach formalizes the 'explanation distribution' derived…
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Research paper distinguishes cross-validation from deep ensembles for AI uncertainty
A new research paper titled "Lost in the Folds" highlights a common misunderstanding in AI research regarding uncertainty estimation in medical image segmentation. The study reveals that using K-fold cross-validation (C…
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AI models show improved blood pressure estimation reliability
Researchers investigated the reliability of uncertainty quantification in deep learning models for blood pressure estimation from photoplethysmography (PPG) signals. The study found that deep ensembles (DE) offer greate…
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Singular Bayesian Neural Networks
Researchers have introduced Singular Bayesian Neural Networks, a novel approach that significantly reduces the parameter count required for Bayesian neural networks. By parameterizing weights using a low-rank decomposit…
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Bayesian deep learning evaluation unstable in low-data settings, studies find
Two new arXiv papers highlight significant instability in evaluating Bayesian deep learning methods, particularly under data scarcity. Researchers found that standard evaluation metrics can produce unreliable and datase…