Pages that link to "Item:Q2051254"
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The following pages link to Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods (Q2051254):
Displaying 28 items.
- Finite electro-elasticity with physics-augmented neural networks (Q2083132) (← links)
- Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression (Q2083714) (← links)
- Multifidelity data fusion in convolutional encoder/decoder networks (Q2099723) (← links)
- How to measure uncertainty in uncertainty sampling for active learning (Q2127220) (← links)
- Information efficient learning of complexly structured preferences: elicitation procedures and their application to decision making under uncertainty (Q2128891) (← links)
- Bayesian-EUCLID: discovering hyperelastic material laws with uncertainties (Q2160432) (← links)
- Handling epistemic and aleatory uncertainties in probabilistic circuits (Q2163184) (← links)
- Lymphoma segmentation from 3D PET-CT images using a deep evidential network (Q2169207) (← links)
- Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons (Q2681129) (← links)
- Epistemic uncertainty quantification in deep learning classification by the delta method (Q6055168) (← links)
- Advanced discretization techniques for hyperelastic physics-augmented neural networks (Q6062433) (← links)
- Model-independent reconstruction of growth index via Gaussian process (Q6065373) (← links)
- DEED: deep evidential doctor (Q6067045) (← links)
- FFNSL: Feed-forward neural-symbolic learner (Q6097158) (← links)
- Responsible model deployment via model-agnostic uncertainty learning (Q6106442) (← links)
- Three-way decision and conformal prediction: isomorphisms, differences and theoretical properties of cautious learning approaches (Q6146030) (← links)
- Neural-physics multi-fidelity model with active learning and uncertainty quantification for GPU-enabled microfluidic concentration gradient generator design (Q6153894) (← links)
- Quality measures for the evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems (Q6153910) (← links)
- Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy (Q6185714) (← links)
- SOAR: simultaneous or-of-and rules for classification of positive and negative classes (Q6548823) (← links)
- Recurrent neural network plasticity models: unveiling their common core through multi-task learning (Q6550155) (← links)
- Machine learning and information theory concepts towards an AI mathematician (Q6554714) (← links)
- Explainability through uncertainty: trustworthy decision-making with neural networks (Q6572867) (← links)
- Synergies between machine learning and reasoning -- an introduction by the Kay R. Amel group (Q6577680) (← links)
- Semi-Structured Distributional Regression (Q6585622) (← links)
- Heteroscedastic Gaussian process regression for material structure-property relationship modeling (Q6609835) (← links)
- Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning (Q6645133) (← links)
- Neural-network-based regularization methods for inverse problems in imaging (Q6664955) (← links)
