Recent advances and applications of deep learning methods in materials science
Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
[HTML][HTML] A review of uncertainty quantification in deep learning: Techniques, applications and challenges
Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …
uncertainties during both optimization and decision making processes. They have been …
Quantifying uncertainty in answers from any language model and enhancing their trustworthiness
We introduce BSDetector, a method for detecting bad and speculative answers from a
pretrained Large Language Model by estimating a numeric confidence score for any output …
pretrained Large Language Model by estimating a numeric confidence score for any output …
Uncertainty estimation of transformer predictions for misclassification detection
Uncertainty estimation (UE) of model predictions is a crucial step for a variety of tasks such
as active learning, misclassification detection, adversarial attack detection, out-of-distribution …
as active learning, misclassification detection, adversarial attack detection, out-of-distribution …
Quantifying uncertainty in answers from any language model via intrinsic and extrinsic confidence assessment
We introduce BSDetector, a method for detecting bad and speculative answers from a
pretrained Large Language Model by estimating a numeric confidence score for any output …
pretrained Large Language Model by estimating a numeric confidence score for any output …
Stochastic neural radiance fields: Quantifying uncertainty in implicit 3d representations
Neural Radiance Fields (NeRF) has become a popular framework for learning implicit 3D
representations and addressing different tasks such as novel-view synthesis or depth-map …
representations and addressing different tasks such as novel-view synthesis or depth-map …
Diversity and generalization in neural network ensembles
Ensembles are widely used in machine learning and, usually, provide state-of-the-art
performance in many prediction tasks. From the very beginning, the diversity of an ensemble …
performance in many prediction tasks. From the very beginning, the diversity of an ensemble …
Fast, accurate, and simple models for tabular data via augmented distillation
Automated machine learning (AutoML) can produce complex model ensembles by stacking,
bagging, and boosting many individual models like trees, deep networks, and nearest …
bagging, and boosting many individual models like trees, deep networks, and nearest …
Neural ensemble search for uncertainty estimation and dataset shift
Ensembles of neural networks achieve superior performance compared to standalone
networks in terms of accuracy, uncertainty calibration and robustness to dataset shift. Deep …
networks in terms of accuracy, uncertainty calibration and robustness to dataset shift. Deep …
Bayesian uncertainty analysis for underwater 3D reconstruction with neural radiance fields
Neural radiance fields (NeRFs) are a deep learning technique that generates novel views of
3D scenes from multi-view images. As an extension of NeRFs, SeaThru-NeRF mitigates the …
3D scenes from multi-view images. As an extension of NeRFs, SeaThru-NeRF mitigates the …