Abstract: Deep-learning-based health prognostics is receiving ever-increasing attention. Most existing methods leverage advanced neural networks for prognostics performance improvement, providing ...

Abstract: Sparse Bayesian learning (SBL) and specifically relevance vector machines have received much attention in the machine learning literature as a means of achieving parsimonious representations ...

Customer stories Events & webinars Ebooks & reports Business insights GitHub Skills ...

https://proceedings.neurips.cc/paper_files/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html ...

Machine learning is the ability of a machine to improve its performance based on previous results. Machine learning methods enable computers to learn without being explicitly programmed and have ...

Leaf functional traits—chlorophyll (CHL), carotenoid (CAR), equivalent water thickness (EWT), nitrogen (N), and leaf mass per ...

Combining various data sources and other types of information is becoming increasingly important in various types of analyses. Certain classes of Bayesian hierarchical models have shown to be ...

A systematic review newly published in the Journal of Pipeline Science and Engineering maps machine learning (ML) advances for pipelines across the full lifecycle: reliability-based design, structural ...

Three funds filed to let software run the portfolio. The sales pages promise a lot. The risk pages quietly take most of it back.

An end-to-end machine learning pipeline for air quality prediction and analysis. Features data preprocessing, feature engineering, Analog Ensemble, LSTM, Bayesian Correction, and Explainable AI. - ...

Explore predictive modeling for compound prioritization, including in silico screening, toxicology models, and lead selection ...