Results 1 to 10 of about 2,475,679 (305)
Uncertainty-aware molecular dynamics from Bayesian active learning for phase transformations and thermal transport in SiC [PDF]
npj Computational Materials, 2023 Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomistic dynamics.
Yu Xie +5 more
doaj +2 more sources
Risk, unexpected uncertainty, and estimation uncertainty: Bayesian learning in unstable settings. [PDF]
PLoS Computational Biology, 2011 Recently, evidence has emerged that humans approach learning using Bayesian updating rather than (model-free) reinforcement algorithms in a six-arm restless bandit problem. Here, we investigate what this implies for human appreciation of uncertainty.
Elise Payzan-LeNestour, Peter Bossaerts
doaj +8 more sources
Amortized Bayesian Model Comparison With Evidential Deep Learning [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2020 Comparing competing mathematical models of complex processes is a shared goal among many branches of science. The Bayesian probabilistic framework offers a principled way to perform model comparison and extract useful metrics for guiding decisions ...
Stefan T. Radev +5 more
semanticscholar +5 more sources
A Noise-Robust Fast Sparse Bayesian Learning Model [PDF]
Communications in Statistics - Simulation and Computation, 2020 This paper utilizes the hierarchical model structure from the Bayesian Lasso in the Sparse Bayesian Learning process to develop a new type of probabilistic supervised learning approach.
Helgøy, Ingvild M., Li, Yushu
core +5 more sources
A Review on Bayesian Deep Learning in Healthcare: Applications and Challenges
IEEE Access, 2022 In the last decade, Deep Learning (DL) has revolutionized the use of artificial intelligence, and it has been deployed in different fields of healthcare applications such as image processing, natural language processing, and signal processing.
Abdullah A. Abdullah +2 more
doaj +2 more sources
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization [PDF]
International Conference on Artificial Intelligence and Statistics, 2023 In this paper, we conduct a comprehensive study of In-Context Learning (ICL) by addressing several open questions: (a) What type of ICL estimator is learned by large language models?
Yufeng Zhang +3 more
semanticscholar +1 more source
Bayesian Prompt Learning for Image-Language Model Generalization [PDF]
IEEE International Conference on Computer Vision, 2022 Foundational image-language models have generated considerable interest due to their efficient adaptation to downstream tasks by prompt learning. Prompt learning treats part of the language model input as trainable while freezing the rest, and optimizes ...
Mohammad Mahdi Derakhshani +6 more
semanticscholar +1 more source
In-Context Learning through the Bayesian Prism [PDF]
International Conference on Learning Representations, 2023 In-context learning (ICL) is one of the surprising and useful features of large language models and subject of intense research. Recently, stylized meta-learning-like ICL setups have been devised that train transformers on sequences of input-output pairs
Kabir Ahuja, Madhuri Panwar, Navin Goyal
semanticscholar +1 more source
Personalized Federated Learning via Variational Bayesian Inference [PDF]
International Conference on Machine Learning, 2022 Federated learning faces huge challenges from model overfitting due to the lack of data and statistical diversity among clients. To address these challenges, this paper proposes a novel personalized federated learning method via Bayesian variational ...
Xu Zhang +4 more
semanticscholar +1 more source
On Sequential Bayesian Inference for Continual Learning
Entropy, 2023 Sequential Bayesian inference can be used for continual learning to prevent catastrophic forgetting of past tasks and provide an informative prior when learning new tasks.
Samuel Kessler +4 more
doaj +1 more source