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Uncertainty driven active learning of coarse grained free energy models | npj Computational Materials
Nature
Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales.
6 months ago
Meet OmniPred: A Machine Learning Framework to Transform Experimental Design with Universal Regression Models
MarkTechPost
The ability to predict outcomes from a myriad of parameters has traditionally been anchored in specific, narrowly focused regression methods...
4 months ago
Bayesian reconstruction of magnetic resonance images using Gaussian processes | Scientific Reports
Nature
A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images.
11 months ago
Metric Learning and Manifolds: Preserving the Intrinsic Geometry
Microsoft
In recent years, manifold learning has become increasingly popular as a tool for performing non-linear dimensionality reduction.
95 months ago
Tree-Boosting for Spatial Data | by Fabio Sigrist
Towards Data Science
Combining gradient tree-boosting with Gaussian process models for modeling spatial data using GPBoost (Machine Learning; Spatial Statistics;...
39 months ago
Generation and evaluation of synthetic patient data - BMC Medical Research Methodology
BMC Medical Research Methodology
Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably...
50 months ago
Top Tools/Platforms for Hyperparameter Optimization 2023
MarkTechPost
Hyper-parameters are parameters used to regulate how the algorithm behaves while it creates the model. These factors cannot be discovered by...
11 months ago
Active learning for prediction of tensile properties for material extrusion additive manufacturing | Scientific Reports
Nature
Machine learning techniques were used to predict tensile properties of material extrusion-based additively manufactured parts made with...
11 months ago
A Conceptual Explanation of Bayesian Hyperparameter Optimization for Machine Learning
Towards Data Science
Following are four common methods of hyperparameter optimization for machine learning in order of increasing efficiency: I was pretty proud...
72 months ago
Deep Bayesian Gaussian processes for uncertainty estimation in electronic health records | Scientific Reports
Nature
One major impediment to the wider use of deep learning for clinical decision making is the difficulty of assigning a level of confidence to...
32 months ago