Entries by Andreas Holzinger

Transfer Learning to overcome catastrophic forgetting

In machine learning deep convolutional networks (deep learning) are very successful for solving particular problems [1] – at least when having many training samples. Great success has been made recently, e.g. in automatic game playing by AlphaGo (see the nature news here).  As fantastic these approaches are, it should be mentioned that deep learning has […]

Machine Learning & Knowledge Extraction (MAKE) Journal launched

Inaugural Editorial Paper published: Holzinger, A. 2017. Introduction to Machine Learning & Knowledge Extraction (MAKE). Machine Learning and Knowledge Extraction, 1, (1), 1-20, doi:10.3390/make1010001. http://www.mdpi.com/2504-4990/1/1/1 Machine Learning and Knowledge Extraction (MAKE) is an inter-disciplinary, cross-domain, peer-reviewed, scholarly open access journal to provide a platform to support the international machine learning community. It publishes original research […]

Call for Papers: Open Data for Discovery Science (due to July, 31, 2017)

The Journal BMC Medical Informatics and Decision Making (SCI IF (2015): 2,042) invites to submit to a new thematic series on open data for discovery science https://bmcmedinformdecismak.biomedcentral.com/articles/collections/odds Note: Excellent submissions to the IFIP Cross Domain Conference on Machine Learning and Knowledge Discovery (CD-MAKE), (Submission due to May, 15, 2017) relevant to the topics described below, […]

Federated Collaborative Machine Learning

The Google Research Group [1] is always doing awesome stuff, the most recent one is on Federated Learning [2], which enables e.g. smart phones (of course any computational device, and maybe later all internet-of-things, intelligent sensors in either smart hospitals or in smart factories etc.) to collaboratively learn a shared representation model, whilst keeping all […]

Integrated interactomes and pathways in precision medicine by Igor Jurisica, Toronto

Machine learning is the fastest growing field in computer science, and Health Informatics is amongst the greatest application challenges, providing benefits in improved medical diagnoses, disease analyses, and pharmaceutical development – towards future precision medicine. Talk announcement: Friday, 12th May, 2017, 10:00, Seminaraum 137, Parterre, Inffeldgasse 16c Integrated interactomes and pathways in precision medicine by […]

What is machine learning?

Many services of our every day life rely meanwhile on machine learning. Machine learning is a very practical field and provides powerful technologies that allows machines (i.e. computers) to learn from prior data, to extract knowledge, to generalize and to make predictions – similar as we humans can do (see the MAKE intro). There is […]

Machine Learning Guide

The Machine Learing Guide by Tyler RENELLE (Tensor Flow, O-C-Devel) is highly recommendable to my students! This series aims to teach the high level fundamentals of machine learning with a focus on algorithms and some underlying mathematics, which is really great. http://ocdevel.com/podcasts/machine-learning      

Cross Domain Conference for Machine Learning & Knowledge Extraction

cd-make.net Call for Papers – due to May, 15, 2017 http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=61244&copyownerid=17803 Call for Papers due to May, 15, 2017 International IFIP Cross Domain Conference for Machine Learning & Knowledge Extraction CD-MAKE in Reggio di Calabria (Italy) August 29 – September 1, 2017 https://cd-make.net CD stands for Cross-Domain and means the integration and appraisal of different […]

Stan: A probabilistic programming language

A long time ago submitted paper from the Stan developers http://mc-stan.org/ has finally been appeared at the Journal of statistical software: https://www.jstatsoft.org Carpenter, B., Gelman, A., Hoffman, M., Lee, D., Goodrich, B., Betancourt, M., Brubaker, M. A., Guo, J., Li, P. & Riddell, A. 2017. Stan: A probabilistic programming language. Journal of Statistical Software, 76, […]