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in the SciencesMathematicsMax Planck Institute for
Max Planck InstituteConference of the
SPEAKERSMichael ArbelUniversity College London
Nihat AyMPI MiS
Pradeep BanerjeeMPI MiS
Yonatan DuklerUCLA
Asja FischerRuhr-Universität Bochum
Tim GeneweinDeepMind London
Matthias HeinUniversität Tübingen
Frederik KünstnerEPFL
Wuchen LiUCLA
Luigi MalagòRIST
Grégoire MontavonTU Berlin
Razvan PascanuDeepMind London
Johannes RauhMPI MiS
Nico ScherfMPI CBS
Ingo SteinwartUniversität Stuttgart
Maurice WeilerUniversity of Amsterdam
SCIENTIFICORGANIZERGuido MontúfarMPI MiS
ABSTR ACTThis meeting aims to discuss mathematical topics in machine learning and deeplearning and to kickoff the ERC project Deep Learning Theory at MPI MIS.
Deep Learning Theory: Geometric Analysis of Capacity, Optimization, and Generalization for Improving Learning in Deep Neural Networks
Deep Learning is one of the most vibrant areas of contemporary machine learning and one of the most promising approaches to Artifi cial Intelligence. Deep Learning drives the latest systems for image, text, and audio processing,as well as an increasing number of new technologies. The goal of this project is to advance on key open problems in Deep Learning, specifi cally regarding the capacity, optimization, and regularization of these algorithms. The idea is to consolidate a theoretical basis that allows us to pin down the inner workings of the present success of Deep Learning and make it more widely applicable, in particular in situations with limited data and challenging problems in reinforcement learning. The approach is based on the geometry of neural networks and exploits innovative mathematics, drawing on information geometry and algebraic statistics. This is a quite timely and unique proposal which holds promise to vastly streamline the progress of Deep Learning into new frontiers.
The kickoff meeting will take place at:
Max Planck Institute for Mathematics in the SciencesInselstraße 2204103 LeipzigLeibniz Saal
www.mis.mpg.de/dltkm
ADMINISTR ATIVECONTACTValeria HünnigerMail: [email protected]
DEEP LEARNINGTHEORYMarch27 – 29, 2019
KICKOFFMEETING
