In this workshop we will explore how to use physical intuition and ideas to design new classes of machine learning (ML) algorithms. Physics-inspired sampling algorithms could be used to train ML structures or sample the hyper-parameter space (e.g. deep Neural Networks). Additionally, physics-based models such as Ising/Potts models or energy-based models have influenced ML inference frameworks such as Markov Random Fields and Restricted Boltzmann Machines, and we want to continue the discussion to facilitate this innovation transfer. Finally, physical insight could be used to enhance learning in the situation of scarce data by enforcing smoothness, differentiability or other physical properties relevant to a given problem.
Conference-Service.com offers, as part of its business activities, a directory of upcoming scientific and technical meetings. The calendar is published for the convenience of conference participants and we strive to support conference organisers who need to publish their upcoming events. Although great care is being taken to ensure the correctness of all entries, we cannot accept any liability that may arise from the presence, absence or incorrectness of any particular information on this website. Always check with the meeting organiser before making arrangements to participate in an event!