Nature

High-entropy solid electrolytes discovery: a dual-stage machine learning framework bridging atomic configurations and ionic transport properties

The vast majority of current computational methods for inorganic materials are based on periodic boundary conditions, and because of this, even with the benefit of high-throughput computing and ...
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 ...
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 ...
C&EN

Voronoi Entropy as a Ligand Molecular Descriptor of Protein–Ligand Interactions

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. Article Views ...
Nature

Design of high bulk moduli high entropy alloys using machine learning

In this work, the authors have demonstrated the use of machine learning (ML) models in the prediction of bulk modulus for High Entropy Alloys (HEA). For the first time, ML has been used for optimizing ...
sei.cmu

Explainability in Cybersecurity Data Science

Mellon, J., and Worrell, C., 2023: Explainability in Cybersecurity Data Science. Software Engineering Institute blog, Accessed June 24, 2026, https://doi.org/10.58012 ...
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) ...