Dynamic black holes may obey Hawking-style thermodynamics with an alternative entropy measure

Of the known things in the universe, black holes are among the most extreme. They pack huge amounts of mass densely into a ...
GitHub

MachineShop: Machine Learning Models and Tools for R

MachineShop is a meta-package for statistical and machine learning with a unified interface for model fitting, prediction, performance assessment, and presentation of results. Support is provided for ...
C&EN

Atomic Interaction-Based Prediction of Phase Formations in High-Entropy Alloys

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Non-Commercial (NC): Only non-commercial uses of the work are permitted. High-entropy ...
Scientific Research Publishing

Performance Comparison of Vision Transformer- and CNN-Based Image Classification Using Cross Entropy: A Preliminary Application to Lung Cancer Discrimination from CT Images ()

1 Faculty of Informatics, The University of Fukuchiyama, Kyoto, Japan. 2 School of Radiological Technology, Gunma Prefectural College of Health Sciences, Gunma, Japan. 3 School of Health Sciences, ...
GitHub

Single-cell causal network inference based on neighbor cross-mapping entropy

Gene regulatory networks (GRNs) reveal the complex molecular interactions that govern cell state. However, it is challenging for identifying causal relations among genes due to noisy data and ...
Hacker

The Evolution of Decision Trees: From Shannon Entropy to Modern Applications and Specialties

Data Science expert with desire to help companies advance by applying AI for process improvements. The journey to Kaggle’s winning approach started in the mid-20th century, and its development has ...
CWI

Scientific Computing

The Scientific Computing group at CWI develops efficient mathematical methods to simulate and predict real-world phenomena with inherent uncertainties. Such uncertainties arise from e.g. uncertain ...
PNAS

On cheap entropy-sparsified regression learning

Regression learning is one of the long-standing problems in statistics, machine learning, and deep learning (DL). We show that writing this problem as a probabilistic expectation over (unknown) ...