In recent years, deep learning techniques have revolutionized machine learning. The beginning of this revolution, in 2012, was marked by the article ImageNet Classification with Deep Convolutional Networks by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. The article demonstrates how…
The efficient solution of optimization problems is a central task in machine learning and in the larger field of data science. Therefore, we are developing GENO (generic optimization), a domain-specific language (DSL) for generating solution software for various optimization problems…
GENO makes mathematical optimization easily accessible: The desired optimization problem is specified in a natural modeling language and the associated solver is generated simply by pressing a button. In machine learning, it was previously common to write a new solver…
Data Assessment has long used topic models to generate role profiles. In this context, a role profile is a topic that is learned from skills data. Now we have significantly expanded the type of data from which a topic model…
Last year more than 1.4 million derivations were computed by almost 50,000 users. In most cases we don't know the purpose of the derivatives, but sometimes users contact us with suggestions for additional features or just to say thanks. Such…
For the second year in a row DAS Research presents at the world's most important machine learning conference NeurIPS (Conference on Neural Information Processing Systems). This year our colleagues Sören Laue and Joachim Giesen demonstrate generic optimization for Machine Learning…
An optimization problem is given by its variables, its objective function and constraints. The goal is to assign values to the variables so that the value of the objective function is optimized, and all constraints are satisfied. At Data Assessment…
