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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
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
Figure 2: Mappings between the manifold of positive definite matrices...
ResearchGate
Metric learning has been shown to be highly effective to improve the performance of nearest neighbor classification. In this paper, we address the problem...
74 months ago
Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process ...
Nature
Relational linkages connecting process, structure, and properties are some of the most sought after goals in additive manufacturing (AM).
37 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
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
Uncertainty-aware mixed-variable machine learning for materials design | Scientific Reports
Nature
Data-driven design shows the promise of accelerating materials discovery but is challenging due to the prohibitive cost of searching the...
19 months ago
Don't Overfit! II — How to avoid Overfitting in your Machine Learning and Deep Learning Models
Towards Data Science
One of the main objectives of predictive modeling is to build a model that would give accurate predictions on unseen data which will only be...
47 months ago