Prof. Saso Dzeroski (Josef Stefan Institute, Slovenia): Semi-supervised Multi-target Prediction
Prof. Nigel Smart (KU Leuven): Computing on Encrypted Data: How to do the impossible
Dr. Arpit Mittal (Amazon Research Cambridge): Large-scale Fact Extraction and Verification
Prof. Padhraic Smyth (UC Irvine, USA): Deep Learning and Statistics: Connections
Prof. Maarten de Rijke (University of Amsterdam): Differentiable Unbiased Online Learning to Rank Based on joint work with Harrie Oosterhuis.
What happens inside a cell when it is activated, changing, or responding to variations in its environment? Researchers from the VIB-UGent Center for Inflammation Research have developed a map of how to best model these cellular dynamics. Their work not only highlights the outstanding challenges of tracking cells throughout their growth and lifetime, but also pioneers new ways of evaluating computational biology methods that aim to do this.
Prof. Sören Auer (Leibniz Information Centre for Science and Technology and University Library): Towards Knowledge Graph based Representation, Augmentation and Exploration of Scholarly Communication
Dr. Sander Dieleman (Google DeepMind): Generating music in the raw audio domain
Every year, Rxivist compiles a list of the most downloaded bioRxiv preprints. The organization has recently put together the list for 2018. And at number 10, we find a paper from the Yvan Saeys group at the VIB-UGent Center for Inflammation Research. This is the first time VIB research makes it into the top 10 of this list.
Tom Dhaene (IDLab) - Deep learning models are a common tool to identify complex patterns in data. However, they still requires tens of thousands of training samples. In this project we investigate the use of Bayesian models such as the (deep) Gaussian process for deep learning and generative modeling modeling when data is scarce.
Tom Dhaene (IDLab) - Generative models are one of the most promising approaches to learn the world around us. In this project we investigate their potential for the design of linear passive electronic systems. Generative models, an unsupervised machine learning technique, are applied to learn the complete design space and to generate interesting valid designs.
Tony Belpaeme (IDLab) organises the Symposium on Robots for Language Learning from 12 to 13 December 2018 in Istanbul, Turkey.
An event organized by UGent, UZ Gent and Living Tomorrow with several international speakers from Microsoft, IBM Watson, Google Deepmind, ….
Prof. Pieter Abbeel (UC Berkeley): Deep Learning to learn
Prof. Panayiotis Tsaparas (University of Ioannina): Maximizing and moderating opinions in social networks
Prof. Michel Dumontier (Maastricht University): Are we FAIR yet?
Prof. Claire Monteleoni (University of Paris-Saclay and George Washington University): Advances in Climate Informatics: Machine Learning Algorithms for Climate Science
Prof. Johan Suykens(KU Leuven): Deep Restricted Kernel Machines
Prof. Remco Chang (Tufts University): Human Data Interaction: Data Organization, Computation, and Visualization
Prof. Aristides Gionis (Aalto University, Finland): Active network alignment
Prof. Luc De Raedt (KU Leuven): Some tools for automating data science