Apart from simple diagnosis, the study takes an important step toward predictive health monitoring by modeling the risk of ...

In the current era of big data, the volume of information continues to grow at an unprecedented rate, giving rise to the crucial need for efficient ...

INNOptimizer is using latest Bayesian Optimization algorithms and a broad set of analytical tools to guide optimizations with minimum experimentation needed. Don't waste time and money with poor ...

An experimental study shows that already small-scale quantum computers can boost the performance of machine learning algorithms.

One of the current hot research topics is the combination of two of the most recent technological breakthroughs: machine learning and quantum computing. An experimental study shows that already ...

SAVANA uses a machine learning algorithm to identify cancer-specific structural variations and copy number aberrations in long-read DNA sequencing data. The complex structure of cancer genomes ...

The developed algorithms for estimating Gaussian posterior densities are essential for the BONE framework, focusing on practical Bayesian approximation methods including Conjugate updates (Cj), Linear ...

Hyperparameter tuning is a crucial step in the development of machine learning models, as it directly impacts their performance and generalization ability. Traditional methods for hyperparameter ...

Machine learning has seen significant advancements in integrating Bayesian approaches and active learning methods. Two notable research papers contribute to this development: “Bayesian vs.