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Uncertainty Quantification

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Showing 1026–1050 of 2366 papers

TitleStatusHype
Analog In-Memory Computing with Uncertainty Quantification for Efficient Edge-based Medical Imaging Segmentation—0
Adaptive Probabilistic Forecasting of Electricity (Net-)Load—0
Industrial Forecasting with Exponentially Smoothed Recurrent Neural Networks—0
Inference and Uncertainty Quantification for Noisy Matrix Completion—0
DADEE: Well-calibrated uncertainty quantification in neural networks for barriers-based robot safety—0
Bayesian Deep Learning for Discrete Choice—0
Cycles of Thought: Measuring LLM Confidence through Stable Explanations—0
Cycle Consistency-based Uncertainty Quantification of Neural Networks in Inverse Imaging Problems—0
Bayesian Deep Convolutional Encoder-Decoder Networks for Surrogate Modeling and Uncertainty Quantification—0
An Algorithm for Sensor Data Uncertainty Quantification—0
Bayesian data assimilation to support informed decision-making in individualised chemotherapy—0
Current Methods for Drug Property Prediction in the Real World—0
Improving Uncertainty Quantification in Large Language Models via Semantic Embeddings—0
An AI-powered Bayesian generative modeling approach for causal inference in observational studies—0
CUPS: Improving Human Pose-Shape Estimators with Conformalized Deep Uncertainty—0
Adaptive modelling of anti-tau treatments for neurodegenerative disorders based on the Bayesian approach with physics-informed neural networks—0
Incentivize without Bonus: Provably Efficient Model-based Online Multi-agent RL for Markov Games—0
An Uncertainty-aware Loss Function for Training Neural Networks with Calibrated Predictions—0
A Bayesian approach to quantifying uncertainties and improving generalizability in traffic prediction models—0
Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty—0
Crowd Counting with Decomposed Uncertainty—0
CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks—0
An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification—0
Improving Random Forests by Smoothing—0
Covariance-Free Sparse Bayesian Learning—0
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