In this talk I will briefly summarise my two recent papers on the interactions between physics and machine learning. In the paper with V Anagiannis, we exploit the analogy between quantum many-body systems and certain neural networks to analyse the learning process using quantum entanglement. In the second paper with de Haan, Rainone, and Bondesan, we use a continuous flow model to help ameliorate the numerical difficulties in sampling in lattice field theories, which for instance hampers high-precision computations in LQCD.
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Recordings of past talks can be found here: www.youtube.com/channel/UCw9LdPQ5t7Q7muD0qzn70TA