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Modelling chemical processes in explicit solvents with machine learning potentials
Nature
Here, we present a general strategy for generating reactive machine learning potentials to model chemical processes in solution.
1 month ago
The role of morphometric characteristics in predicting 20-meter sprint performance through machine learning
Nature
The aim of this study was to test the morphometric features affecting 20-m sprint performance in children at the first level of primary education using machine...
1 month ago
Generation and evaluation of synthetic patient data
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...
51 months ago
Fig. 4. Data augmentation with Statistical Shape Analysis and Gaussian...
ResearchGate
Figure 4 provides an overview of the data augmentation process. Due to the page limit, the details about SSA and GP modeling will not be discussed in this...
51 months ago
Basics of few-shot learning with optimization-based meta-learning
Towards Data Science
Meta-learning approaches can be broadly classified into metric-based, optimization-based, and model-based approaches. In this post, we will mostly be...
35 months ago
Active learning-assisted neutron spectroscopy with log-Gaussian processes
Nature
We describe a probabilistic active learning approach that not only runs autonomously, ie, without human interference, but can also directly provide locations...
16 months ago
Deep Bayesian Gaussian processes for uncertainty estimation in electronic health records
Nature
We demonstrate that our method is better at capturing uncertainty than both Gaussian processes and deep Bayesian neural networks in terms of indicating data...
34 months ago
Bayesian nonparametric models characterize instantaneous strategies in a competitive dynamic game
Nature
Previous studies of strategic social interaction in game theory have predominantly used games with clearly-defined turns and limited choices...
64 months ago
Mechanical behavior predictions of additively manufactured microstructures using functional Gaussian process surrogates | npj Computational Materials
Nature
This work describes and demonstrates a methodology to link microstructure morphology to mechanical properties using functional Gaussian process surrogate...
39 months ago
Machine learning optimization of candidate antibody yields highly diverse sub-nanomolar affinity antibody libraries
Nature
Here we present an end-to-end Bayesian, language model-based method for designing large and diverse libraries of high-affinity single-chain variable fragments...
14 months ago