Machine learning and LHC event generation

Accelerating the BSM interpretation of LHC data with machine learning

Gianfranco Bertone, Marc Peter Deisenroth, Jong Soo Kim, Sebastian Liem, Roberto Ruiz de Austri, and Max Welling GRAPPA, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, Netherlands Department of Computing, Imperial College London, 180 Queen’s Gate, SW7 2AZ London, United Kingdom Center for Theoretical Physics of the Universe, Institute for Basic Science (IBS), Daejeon, 34051, Kore...

Cardiovascular Event Prediction by Machine Learning

Next Generation Artificial Intelligence and Machine Learning

Event Streams Clustering Using Machine Learning Techniques

Data streams are usually of unbounded lengths which push users to consider only recent observations by focusing on a time window, and ignore past data. However, in many real world applications, past data must be taken in consideration to guarantee the efficiency, the performance of decision making and to handle data streams evolution over time. In order to build a selectively history to track t...

Non-textual Event Summarization by Applying Machine Learning to Template-based Language Generation

We describe a learning-based system that creates draft reports based on observation of people preparing such reports in a target domain (conference replanning). The reports (or briefings) are based on a mix of text and event data. The latter consist of task creation and completion actions, collected from a wide variety of sources within the target environment. The report drafting system is part...