Cover Image

Multi-level Monte Carlo Dropout for Efficient Uncertainty Quantification

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Pim, Aaron, Pryer, Tristan
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Surrogate Modelling of Proton Dose with Monte Carlo Dropout Uncertainty Quantification
by: Pim, Aaron, et al.
Published: (2025)

Unreliable Uncertainty Estimates with Monte Carlo Dropout
by: Djupskås, Aslak, et al.
Published: (2025)

Enhancing Uncertainty Estimation in Semantic Segmentation via Monte-Carlo Frequency Dropout
by: Zeevi, Tal, et al.
Published: (2025)

Dirichlet-Based Monte Carlo Dropout for Uncertainty Estimation in Neural Networks
by: Hoblos, Rouaa, et al.
Published: (2026)

Customer Lifetime Value Prediction with Uncertainty Estimation Using Monte Carlo Dropout
by: Cao, Xinzhe, et al.
Published: (2024)

Enhancing Monte Carlo Dropout Performance for Uncertainty Quantification
by: Asgharnezhad, Hamzeh, et al.
Published: (2025)

Uncertainty Quantification In Surface Landmines and UXO Classification Using MC Dropout
by: Lekhak, Sagar, et al.
Published: (2025)

Enhancing Fairness and Performance in Machine Learning Models: A Multi-Task Learning Approach with Monte-Carlo Dropout and Pareto Optimality
by: Zanna, Khadija, et al.
Published: (2024)

Physics-Informed Neural Networks for Methane Sorption: Cross-Gas Transfer Learning, Ensemble Collapse Under Physics Constraints, and Monte Carlo Dropout Uncertainty Quantification
by: Nooraiepour, Mohammad, et al.
Published: (2026)

Robust Uncertainty Quantification Using Conformalised Monte Carlo Prediction
by: Bethell, Daniel, et al.
Published: (2023)

Monte Carlo Permutation Search
by: Cazenave, Tristan
Published: (2025)

Uncertainty-Aware Genomic Classification of Alzheimer's Disease: A Transformer-Based Ensemble Approach with Monte Carlo Dropout
by: Jo, Taeho, et al.
Published: (2025)

Enhanced Data-Driven Product Development via Gradient Based Optimization and Conformalized Monte Carlo Dropout Uncertainty Estimation
by: Nava, Andrea Thomas, et al.
Published: (2026)

Deep Uzawa for Kinetic Transport with Lagrange-Enforced Boundaries
by: Makridakis, Charalambos, et al.
Published: (2025)

Efficient Stochastic Optimisation via Sequential Monte Carlo
by: Cuin, James, et al.
Published: (2026)

Rethinking Inter-LoRA Orthogonality in Adapter Merging: Insights from Orthogonal Monte Carlo Dropout
by: Zhang, Andi, et al.
Published: (2025)

An Isotropic Approach to Efficient Uncertainty Quantification with Gradient Norms
by: Grünefeld, Nils, et al.
Published: (2026)

Accurate Large-sample Uncertainty Quantification using Stochastic Gradient Markov Chain Monte Carlo
by: Wang, Yu, et al.
Published: (2026)

Large-scale Uncertainty Quantification for Latent Variable Models Using Subsampling Markov Chain Monte Carlo
by: Wang, Xiaoyu, et al.
Published: (2026)

DPad: Efficient Diffusion Language Models with Suffix Dropout
by: Chen, Xinhua, et al.
Published: (2025)

Automated Efficient Estimation using Monte Carlo Efficient Influence Functions
by: Agrawal, Raj, et al.
Published: (2024)

Optimal control of a kinetic equation
by: Pim, Aaron, et al.
Published: (2024)

Bayesian Neural Networks with Monte Carlo Dropout for Probabilistic Electricity Price Forecasting
by: Das, Abhinav, et al.
Published: (2025)

Multi-fidelity Hamiltonian Monte Carlo
by: Patel, Dhruv V., et al.
Published: (2024)

Multi-group Uncertainty Quantification for Long-form Text Generation
by: Liu, Terrance, et al.
Published: (2024)

Variational Inference for Uncertainty Quantification: an Analysis of Trade-offs
by: Margossian, Charles C., et al.
Published: (2024)

Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models
by: Cao, Qi, et al.
Published: (2026)

Counterdiabatic Hamiltonian Monte Carlo
by: Cohn-Gordon, Reuben, et al.
Published: (2026)

Elements of Sequential Monte Carlo
by: Naesseth, Christian A., et al.
Published: (2019)

Uncertainty as Feature Gaps: Epistemic Uncertainty Quantification of LLMs in Contextual Question-Answering
by: Bakman, Yavuz, et al.
Published: (2025)

Testing Spintronics Implemented Monte Carlo Dropout-Based Bayesian Neural Networks
by: Ahmed, Soyed Tuhin, et al.
Published: (2024)

Coreset Markov Chain Monte Carlo
by: Chen, Naitong, et al.
Published: (2023)

Uncertainty Quantification for In-Context Learning of Large Language Models
by: Ling, Chen, et al.
Published: (2024)

Estimating Semantic Alphabet Size for LLM Uncertainty Quantification
by: McCabe, Lucas H., et al.
Published: (2025)

Uncertainty Quantification in Graph Neural Networks with Shallow Ensembles
by: Vinchurkar, Tirtha, et al.
Published: (2025)

Rate-In: Information-Driven Adaptive Dropout Rates for Improved Inference-Time Uncertainty Estimation
by: Zeevi, Tal, et al.
Published: (2024)

Enhancing Vision-Language Model Reliability with Uncertainty-Guided Dropout Decoding
by: Fang, Yixiong, et al.
Published: (2024)

Flow Matching with Uncertainty Quantification and Guidance
by: Han, Juyeop, et al.
Published: (2026)

Calibrated Physics-Informed Uncertainty Quantification
by: Gopakumar, Vignesh, et al.
Published: (2025)

Multi-Fidelity Bayesian Neural Network for Uncertainty Quantification in Transonic Aerodynamic Loads
by: Vaiuso, Andrea, et al.
Published: (2024)