Thanks to a diversified program of collaborations with leading ICT companies and other research organisations, CERN openlab promotes research on innovative solutions and knowledge sharing between communities. In particular, it is involved in a large set of Deep Learning and AI projects within the High Energy Physics community and beyond. The HEP community has a long tradition of using Neural...
Deep Learning techniques are being studied for different applications by the HEP community: in this talk, we discuss the case of detector simulation. The need for simulated events, expected in the future for LHC experiments and their High Luminosity upgrades, is increasing dramatically and requires new fast simulation solutions. We will describe an R&D activity within CERN openlab, aimed at...
Deep learning is widely used in many problem areas, namely computer vision, natural language processing, bioinformatics, biomedicine, and others. Training neural networks involves searching the optimal weights of the model. It is a computationally intensive procedure, usually performed a limited number of times offline on servers equipped with powerful graphics cards. Inference of deep models...
Deep Learning is revolutionizing the fields of computer vision, speech recognition and control systems. In recent years, a number of scientific domains (climate, high-energy physics, nuclear physics, astronomy, cosmology, etc) have explored applications of Deep Learning to tackle a range of data analytics problems. As one attempts to scale Deep Learning to analyze massive scientific datasets...
We will present a partnership between CERN, Intel, and the United Nations Institute for Training and Research (UNITAR) to use Deep Learning (DL) to improve the analysis of optical satellite imagery for humanitarian purposes.
Our core objective is to create spectrally valid simulated high-resolution satellite imagery depicting humanitarian situations such as refugee settlements, flood...
