Overviews of Graph Classification and Network Clustering methods

Thanks to Sebastian Raschka I am able to share this great GitHub overview page of relevant graph classification techniques, and the scientific papers behind them. The overview divides the algorithms into four groups: Factorization Spectral and Statistical Fingerprints Deep Learning Graph Kernels Moreover, the overview contains links to similar collections on community detection, classification/regression trees and gradient boosting papers…

2019 Shortlist for the Royal Society Prize for Science Books

Since 1988, the Royal Society has celebrated outstanding popular science writing and authors. Each year, a panel of expert judges choose the book that they believe makes popular science writing compelling and accessible to the public. Over the decades, the Prize has celebrated some notable winners including Bill Bryson and Stephen Hawking. The author of the winning…

Glossary of Statistical Terminology

Frank Harrel shared this 16-page glossary of statistical terminology created by the Department of Biostatistics of Vanderbilt University School of Medicine. The overview touches on everything from Bayes’ Theorem to p-values, explaining matters in just the right detail. Various study designs and model types are also discussed so it might just come in handy for…

Papers with Code: State-of-the-Art

OK, this is a really great find! The website PapersWithCode.com lists all scientific publications of which the codes are open-sourced on GitHub. Moreover, you can sort these papers by the stars they accumulated on Github over the past days. The authors, @rbstojnic and @rosstaylor90, just made this in their spare time. Thank you, sirs! Papers with Code allows you to quickly…

Checklist to Optimize Training Transfer in Organizations

Ashley Hughes, Stephanie Zajac, Jacqueline Spencer, and Eduardo Salas wrote a recent research note for the International Journal of Training and Development. The research note is build around an evidence-based checklist of actionable insights for practitioners that will help to enhance the effectiveness of training interventions. These actionable insights would help to prevent ‘transfer problem’, meaning that…

Privacy, Compliance, and Ethical Issues with Predictive People Analytics

November 9th 2018, I defended my dissertation on data-driven human resource management, which you can read and download via this link. On page 149, I discuss several of the issues we face when implementing machine learning and analytics within an HRM context. For the references and more detailed background information, please consult the full dissertation. More interesting reads on ethics in machine learning can be found here….

10 Simple Rules for Better Data Visualizations

Nicolas Rougier, Michael Droettboom, Philip Bourne wrote an open access article for the Public Library of Open Science (PLOS) in 2014, proposing ten simple rules for better figures. Below I posted these 10 rules and quote several main sentences extracted from the original article. Rule 1: Know Your Audience It is important to identify, as early…

Computers decode what humans see: Generating images from brain activity

I recently got pointed towards a 2017 paper on bioRxiv that blew my mind: three researchers at the Computational Neuroscience Laboratories at Kyoto, Japan, demonstrate how they trained a deep neural network to decode human functional magnetic resonance imaging (fMRI) patterns and then generate the stimulus images. In simple words, the scholars used sophisticated machine learning to…

Bayesian data analysis for newcomers

Professor John Kruschke and Torrin Liddell – one of his Ph.D. students at Indiana University – wrote a fantastically useful scientific paper introducing Bayesian data analysis to the masses. Kruschke and Liddell explain the main ideas behind Bayesian statistics, how Bayesians deal with continuous and binary variables, how to use and set meaningful priors, the differences between…