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.
An agenda, such diversified and interdisciplinary, ACEAI and the 8th ISFAS sincerely invite all relevant professionals in both academic and the industry to present and to exchange. All relevant abstracts/full papers in regards to “Fundamental and Applied Sciences” and “Engineering and Information” are welcomed, especially in the fields of AI, 5G, IoT and Blockchain.
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