Preprint2024
Fortuna: A library for uncertainty quantification in deep learning
Unknown
Fortuna is an open-source library for uncertainty quantification in deep learning, addressing the gap between theory and practice.
0Jan 1, 2024
AI Research
AI & ML Research Papers
A comprehensive index of artificial intelligence and machine-learning research with AI-generated summaries, citation metrics, and direct links to papers and code.
All Papers
24 papersPreprint2022
Autodeuq: Automated deep ensemble with uncertainty quantification
Unknown
AutoDEUQ automates deep ensemble construction with uncertainty quantification, evaluated on 10 regression benchmark datasets.
0Jan 1, 2022Benchmarks
Preprint2022
Nonparametric uncertainty quantification for single deterministic neural network
Unknown
Proposes a fast, scalable nonparametric method to quantify uncertainty in predictions from a single deterministic neural network.
0Jan 1, 2022Neural Networks
Preprint2024
Efficient and effective uncertainty quantification for LLMs
Unknown
The paper presents methods for efficient and effective uncertainty quantification in large language models, aiming to provide scalar estimates of answer correctness.
0Jan 1, 2024
Preprint2020
Uncertainty quantification using Bayesian neural networks in classification: Application to biomedical image segmentation
Unknown
The paper proposes a Bayesian neural network method for uncertainty quantification in classification, applied to biomedical image segmentation.
0Jan 1, 2020Neural Networks
Preprint2023
Generating with confidence: Uncertainty quantification for black-box large language models
Unknown
This paper explores uncertainty quantification for black-box large language models in natural language generation, addressing a previously under-explored problem.
0May 1, 2023Large Language Models
arXiv
Preprint2026
Know What You Don't Know: Uncertainty Calibration of Process Reward Models
Unknown
The paper analyzes uncertainty calibration of process reward models, extending evaluation to a 72B parameter variant on MATH500 and AIME24-25 datasets.
0Jan 1, 2026
PreprintACM Computing Surveys1980
The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty
Lee D. Erman, Frederick Hayes‐Roth, Victor Lesser, et al.
Hearsay-II introduced a blackboard-style framework that coordinates independent knowledge sources to resolve uncertainty in speech understanding, influencing cooperative AI problem-solving.
1.5kJun 1, 1980
Preprint2023
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons
Unknown
This paper presents methods, metrics, and comparisons for uncertainty quantification in scientific machine learning, along with an open-source Python library.
0Jan 1, 2023
Preprint2019
Using self-supervised learning can improve model robustness and uncertainty
Unknown
Applying self-supervised learning to improve robustness and uncertainty of deep learning models beyond purely supervised methods.
0Jan 1, 2019
Preprint2024
Uncertainty-aware reward model: Teaching reward models to know what is unknown
Unknown
This paper introduces an uncertainty-aware reward model that teaches reward models to recognize and quantify their own uncertainty, improving alignment of LLMs with human expectations.
0Oct 1, 2024Large Language Models
arXiv
Preprint2023
Scalable safe long-horizon planning in dynamic environments leveraging conformal prediction and temporal correlations
Unknown
This paper introduces a scalable safe long-horizon planning method that uses copulas to model temporal correlations in uncertainty, providing probabilistic guarantees via conformal prediction.
0Jan 1, 2023