A new hybrid deep learning model tuned by a snake-inspired optimization algorithm achieves 98.13 percent accuracy in ...

After testing 100 hyperparameter candidates, the best accuracy was 92.5%. But is that value really "optimal"? In reality, it ...

Billions of low-power devices now form the nervous system of modern infrastructure, from smart meters and industrial sensors to medical monitors and connected vehicles. But the very protocols that let ...

Genomic prediction has become an important approach for accelerating crop breeding by using genome-wide marker information to predict complex traits. However, the performance of genomic prediction ...

Clarifying gene regulatory networks (GRNs) remains one of the central challenges of systems biology and is crucial for elucidating pathogenesis and curing diseases. Various machine learning techniques ...

This code provides a hyper-parameter optimization implementation for machine learning algorithms, as described in the paper: L. Yang and A. Shami, “On hyperparameter optimization of machine learning ...

At some point in your career, whether you’re making an investor pitch or hosting a webinar, you’ll likely need to present information in front of an audience. Fortunately, you can draw on a range of ...

When building machine learning models with moderate to high complexity, there is an ample range of model parameters that are not learned from data, but instead must be set by us a priori: these are ...

A model's hyperparameters control its capacity and training behavior. Defaults are a useful baseline, not necessarily the best configuration for a particular dataset. Tuning tests alternatives under a ...

Abstract: In this letter, we propose a hyperparameter optimization method for adaptive filtering based on deep unrolling, termed the deep unrolling affine projection (DAP) algorithm. The core idea is ...