scholarly journals Predictive Capability Assessment of Probabilistic Machine Learning Models for Density Prediction of Conventional and Synthetic Jet Fuels

2021 ◽  
Vol 35 (3) ◽  
pp. 2520-2530 ◽  
Author(s):  
Clemens Hall ◽  
Bastian Rauch ◽  
Uwe Bauder ◽  
Patrick Le Clercq ◽  
Manfred Aigner
Keyword(s):  
Machine Learning ◽  
Synthetic Jet ◽  
Jet Fuels ◽  
Learning Models ◽  
Predictive Capability ◽  
Density Prediction ◽  
Probabilistic Machine Learning ◽  
Machine Learning Models

scholarly journals Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles

2017 ◽  
Vol 32 (5) ◽  
pp. 1819-1840 ◽  
Author(s):  
David John Gagne ◽  
Amy McGovern ◽  
Sue Ellen Haupt ◽  
Ryan A. Sobash ◽  
John K. Williams ◽  
...  
Keyword(s):  
Machine Learning ◽  
Size Distribution ◽  
Prediction Models ◽  
Weather Prediction ◽  
Radar Data ◽  
Object Identification ◽  
Atmospheric Conditions ◽  
Learning Models ◽  
Probabilistic Machine Learning ◽  
Machine Learning Models

Abstract Forecasting severe hail accurately requires predicting how well atmospheric conditions support the development of thunderstorms, the growth of large hail, and the minimal loss of hail mass to melting before reaching the surface. Existing hail forecasting techniques incorporate information about these processes from proximity soundings and numerical weather prediction models, but they make many simplifying assumptions, are sensitive to differences in numerical model configuration, and are often not calibrated to observations. In this paper a storm-based probabilistic machine learning hail forecasting method is developed to overcome the deficiencies of existing methods. An object identification and tracking algorithm locates potential hailstorms in convection-allowing model output and gridded radar data. Forecast storms are matched with observed storms to determine hail occurrence and the parameters of the radar-estimated hail size distribution. The database of forecast storms contains information about storm properties and the conditions of the prestorm environment. Machine learning models are used to synthesize that information to predict the probability of a storm producing hail and the radar-estimated hail size distribution parameters for each forecast storm. Forecasts from the machine learning models are produced using two convection-allowing ensemble systems and the results are compared to other hail forecasting methods. The machine learning forecasts have a higher critical success index (CSI) at most probability thresholds and greater reliability for predicting both severe and significant hail.

Hardware-Aware Probabilistic Machine Learning Models

2021 ◽  
Author(s):  
Laura Isabel Galindez Olascoaga ◽  
Wannes Meert ◽  
Marian Verhelst
Keyword(s):  
Machine Learning ◽  
Learning Models ◽  
Probabilistic Machine Learning ◽  
Machine Learning Models

scholarly journals Probabilistic Machine Learning: Models, Algorithms and a Programming Library

2018 ◽  
Author(s):  
Jun Zhu
Keyword(s):  
Machine Learning ◽  
Representation Learning ◽  
Learning Models ◽  
Uncertain Environments ◽  
Modeling Uncertainty ◽  
Programming Library ◽  
Inference Algorithms ◽  
Scalable Inference ◽  
Probabilistic Machine Learning ◽  
Machine Learning Models

Probabilistic machine learning provides a suite of powerful tools for modeling uncertainty, performing probabilistic inference, and making predictions or decisions in uncertain environments. In this paper, we present an overview of our recent work on probabilistic machine learning, including the theory of regularized Bayesian inference, Bayesian deep learning, scalable inference algorithms, a probabilistic programming library named ZhuSuan, and applications in representation learning as well as learning from crowds.

scholarly journals Probabilistic Machine Learning for Healthcare

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Irene Y. Chen ◽  
Shalmali Joshi ◽  
Marzyeh Ghassemi ◽  
Rajesh Ranganath
Keyword(s):  
Machine Learning ◽  
Data Science ◽  
Model Building ◽  
Generative Models ◽  
Annual Review ◽  
Publication Date ◽  
Biomedical Data ◽  
Learning Models ◽  
Probabilistic Machine Learning ◽  
Machine Learning Models

Machine learning can be used to make sense of healthcare data. Probabilistic machine learning models help provide a complete picture of observed data in healthcare. In this review, we examine how probabilistic machine learning can advance healthcare. We consider challenges in the predictive model building pipeline where probabilistic models can be beneficial, including calibration and missing data. Beyond predictive models, we also investigate the utility of probabilistic machine learning models in phenotyping, in generative models for clinical use cases, and in reinforcement learning. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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