Deep Learning Applications in the Natural Sciences

Prof. Pierre Baldi
USA, Department of Computer Science, Center for Machine Learning and Intelligent Systems, and Institute for Genomics and Bioinformatics at University of California, Irvine
Machine learning has been one of the main success stories of computer science over the last few decades. Today, the cutting edge of machine learning is deep learning, and deep learning has been key to designing intelligent systems that can leverage big data to address a host of engineering applications ranging from computer vision, to robotics, to natural language understanding, and to speech recognition.
We will present recent developments in the theory of deep learning and the application of deep learning methods to several problems in the natural sciences, including:
  1. the detection of exotic particles in high-energy physics;
  2. the prediction of the physical, chemical, and biological properties of small molecules and the prediction of chemical reactions in chemistry; and
  3. the prediction of the structural features and 3D structures of proteins in biology.