Nanowerk

Atomic jitters power a new kind of probabilistic AI chip

Thermal noise in magnetic tunnel junctions, usually suppressed, now serves as a tunable source of randomness for Bayesian ...
Tech Xplore

Five-level model rates humanoid robots across mobility, manipulation and cognition

A research team from Fraunhofer HNFIZ has published a newly developed evaluation model that classifies the technical ...
BMJ

Estimating prevalence and predictors of glucose-lowering overtreatment among older adults with type 2 diabetes in long-term care and community settings: a machine learning ...

Objective To estimate the prevalence of potential overtreatment of type 2 diabetes mellitus (T2DM) among older adults and to develop and compare predictive models to identify patient and physician ...
Scientific Research Publishing

Comparative Analysis of Neural Networks and Naive Bayes for Multilingual Text Identification ()

1 Department of Computer Science, Rochester Institute of Technology, Rochester, USA. 2 Department of Computer Science, Rutgers University, New Brunswick, USA. Language identification is a fundamental ...
Scientific Research Publishing

Comparing Classification Models for Predicting Malaria: A Case Study of Malaria Incidence in Kenya ()

1 Department of Mathematics, Meru University of Science & Technology (MUST), Meru, Kenya. 2 Department of Mathematics, Pan African University Institute of Basic Sciences Technology and Innovations ...
www.cs.cmu.edu

Classical generalization theory is more predictive in foundation models than in conventional deep networks

Key takeaway: When learning over discrete prompts used in modern vision-language models, classical PAC-Bayes bounds turn out to be remarkably tight — significantly tighter than prior PAC-Bayes results ...
JSTOR Daily

Learning from a lot: Empirical Bayes for high-dimensional model-based prediction

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...
Frontiers

Advanced machine learning artificial neural network classifier for lithology identification using Bayesian optimization

Identifying lithology is crucial for geological exploration, and the adoption of artificial intelligence is progressively becoming a refined approach to automate this process. A key feature of this ...