DaDaDa 2016: Felix Friedmann – Semantic Segmentation

Abstract:  With the rise of Deep Learning, paradigms have changed in several well-established fields of computer science. Algorithms that had been developed for decades were instantly outperformed by deep neural networks and often levels of performance were reached that seemed unimaginable. Computer Vision was one of the first areas that were conquered by Deep Learning. AlexNet, a deep Convolutional Neural[…]

DaDaDa 2016: Daniel Pettersson – Bayesian Semantics: Verb Sense Induction

Abstract: Bayesian models, such as topic modeling (LDA), have had enormous impact on natural language processing. Although deep neural architectures have improved performance on many tasks, there are still many problems that lend themselves best to a Bayesian treatment. In this talk, we will motivate and develop a Bayesian model for verb sense induction, based[…]

DaDaDa2016: Giuseppe Casaliccio – Introducing an R package to interface the OpenML platform 

Abstract: OpenML is an online machine learning platform where researchers can automatically log and share data, code, and experiments, and organize them online to work and collaborate more effectively. We present an R package to interface the OpenML platform and illustrate its usage both as a stand-alone package and in combination with the mlr machine[…]

DaDaDa2016: Andreas Groll – Modeling Football Results Using Match-specific Covariates

Abstract: Modeling Football Results Using Match-specific Covariates A model for results of football matches is proposed that is able to take into account match-specifi c covariates as, for example, the total distance a team runs in the specifi c match. The model extends the Bradley-Terry model in many diff erent ways. In addition to the inclusion of covariates,[…]

DaDaDa2016: Daniel Weimer – Deep Convolutional Neural Networks in industrial applications

Abstract:  Deep Learning is a new (and at the same time old) paradigm in machine learning which allows to extract features directly from huge amounts of raw data with a minimum of human interaction. This talk gives an introduction about deep learning in general and focuses an important application of deep convolutional neural networks (CNN)[…]