Found 4006 results, showing the newest relevant preprints. Sort by relevancy only.Update me on new preprints

Accurate Prediction of Chemical Shifts for Aqueous Protein Structure for "Real World" Cases using Machine Learning

Our UCBShift predictor implements two modules: a transfer prediction module that employs both sequence and structural alignment to select reference candidates for experimental chemical shift replication, and a redesigned machine learning module based on random forest regression which utilizes more, and more carefully... Expand abstract.
53 days ago
10/10 relevant
arXiv

Merlin: Enabling Machine Learning-Ready HPC Ensembles

With the growing complexity of computational and experimental facilities, many scientific researchers are turning to machine learning (ML) techniques to analyze large scale ensemble data. Expand abstract.
53 days ago
10/10 relevant
arXiv

Combining machine learning and a universal acoustic feature-set yields efficient automated monitoring of ecosystems

Our highly generalisable approach, and the common set of features, will enable scientists to unlock previously hidden insights from eco-acoustic data and offers promise as a backbone technology for global collaborative autonomous ecosystem monitoring efforts. Expand abstract.
53 days ago
8/10 relevant
bioRxiv

Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling

In order to handle the big training data sets in large-scale machine learning, we develop a stochastic gradient version of QHMC using Nos\'e-Hoover thermostat called QSGNHT, and we also provide theoretical justifications about its steady-state distributions. Expand abstract.
54 days ago
4/10 relevant
arXiv

Physics-Informed Machine Learning with Conditional Karhunen-Lo\`eve Expansions

We present a new physics-informed machine learning approach for the inversion of PDE models with heterogeneous parameters. Expand abstract.
54 days ago
10/10 relevant
arXiv

State estimation of surface and deep flows from sparse SSH observations of geostrophic ocean turbulence using Deep Learning

Here we explore the possibility of SSH interpolation using Deep Learning — a machine learning approach that extracts information only from data. Expand abstract.
54 days ago
4/10 relevant
EarthArXiv

Machine Learning for Paper Grammage Prediction Based on Sensor Measurements in Paper Mills

Machine Learning (ML) algorithms can effectively be used to resolve this tradeoff between full automation and human assistance. Expand abstract.
55 days ago
10/10 relevant
Preprints.org

An Introduction to Communication Efficient Edge Machine Learning

Intelligence can be distilled from the data to support next-generation AI-powered applications, which is called edge machine learning. Expand abstract.
55 days ago
9/10 relevant
arXiv

Make Thunderbolts Less Frightening -- Predicting Extreme Weather Using Deep Learning

Machine learning approaches and especially deep learning have however shown huge improvements in many research areas dealing with large datasets in recent years. Expand abstract.
55 days ago
5/10 relevant
arXiv

Predicting Lake Erie Wave Heights using XGBoost

In this study, we applied and tested a novel machine learning method based on XGBoost for predicting waves in Lake Erie in 2016-2017. Expand abstract.
55 days ago
4/10 relevant
arXiv