Cover Image

Enhanced Data-Driven Product Development via Gradient Based Optimization and Conformalized Monte Carlo Dropout Uncertainty Estimation

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Nava, Andrea Thomas, Johny, Lijo, Azzalini, Fabio, Schneider, Johannes, Casanova, Arianna
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

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

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

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

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

Multi-level Monte Carlo Dropout for Efficient Uncertainty Quantification
by: Pim, Aaron, et al.
Published: (2026)

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

TSB: A Time-Saved Benchmark for AI Systems — Measuring Net Productivity Impact Across Knowledge Work
by: Shalom Lijo, Solomon
Published: (2026)

Towards Trustworthy Breast Tumor Segmentation in Ultrasound using Monte Carlo Dropout and Deep Ensembles for Epistemic Uncertainty Estimation
by: Musah, Toufiq, et al.
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)

A Deep Learning Approach for Transient Electromagnetic Data Denoising, Inversion and Uncertainty Analysis With Monte Carlo Dropout Technique
by: Yinjia Zhu, et al.
Published: (2025)

Bitcoin Price Prediction Using Deep Bayesian LSTM With Uncertainty Quantification: A Monte Carlo Dropout–Based Approach
by: Masoud Muhammed Hassan
Published: (2024)

Exploring Human-LLM Conversations: Mental Models and the Originator of Toxicity
by: Schneider, Johannes, et al.
Published: (2024)

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

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

The Compound Infeasibility of AGI: Why Replicating and Exceeding Human Cognition Is Not a Scaling Problem
by: Shalom Lijo, Solomon
Published: (2026)

Chaos for foliated spaces and pseudogroups
by: Lijó, Ramón Barral
Published: (2022)

El privilegio ambiental metropolitano en las emisiones de gases de efecto invernadero. Estudio de caso sobre Galicia y Madrid
by: Enrique Pérez Lijó
Published: (2019)

Adaptive Dropout for Pruning Conformers
by: Kubo, Yotaro, et al.
Published: (2024)

X-ray spectral fitting with Monte Carlo Dropout Neural Networks
by: Tutone, A., et al.
Published: (2025)

On the use of splines for representing ordered factors
by: Azzalini, Adelchi
Published: (2024)

Asymptotics of Stochastic Gradient Descent with Dropout Regularization in Linear Models
by: Li, Jiaqi, et al.
Published: (2024)

Refined Gradient-Based Temperature Optimization for the Replica-Exchange Monte-Carlo Method
by: Miyata, Tatsuya, et al.
Published: (2026)

An Interpretable Deep-Learning Framework for Predicting Hospital Readmissions From Electronic Health Records
by: Azzalini, Fabio, et al.
Published: (2023)

Monte Carlo Stochastic Depth for Uncertainty Estimation in Deep Learning
by: Müller, Adam T., et al.
Published: (2026)

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

Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation
by: Zhao, Yidong, et al.
Published: (2024)

A Monte Carlo Framework for Calibrated Uncertainty Estimation in Sequence Prediction
by: Yang, Qidong, et al.
Published: (2024)

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)

On the Impact of Monte Carlo Statistical Uncertainty on Surrogate-based Design Optimization
by: Erdem, Omer F., et al.
Published: (2025)

Optimized multilevel Monte Carlo methods in Banach spaces
by: Kirchner, Kristin, et al.
Published: (2026)

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

Scale-Dropout: Estimating Uncertainty in Deep Neural Networks Using Stochastic Scale
by: Ahmed, Soyed Tuhin, et al.
Published: (2023)

Gradient-Based Markov Chain Monte Carlo for MIMO Detection
by: Zhou, Xingyu, et al.
Published: (2023)

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

Bayesian Optimization-based Tire Parameter and Uncertainty Estimation for Real-World Data
by: Goblirsch, Sven, et al.
Published: (2025)

MontePrep: Monte-Carlo-Driven Automatic Data Preparation without Target Data Instances
by: Ge, Congcong, et al.
Published: (2025)

Polysemantic Dropout: Conformal OOD Detection for Specialized LLMs
by: Gupta, Ayush, et al.
Published: (2025)

Optimization Results of Dropout-Based Genetic Algorithms
by: Ganci, Daniele
Published: (2025)

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)