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SUMMARY:Deep learning\, Monte Carlo and Quantum Mechanics - Alex Matthews\
 , DeepMind
DTSTART:20231130T130000Z
DTEND:20231130T140000Z
UID:TALK205069@talks.cam.ac.uk
CONTACT:Gaurav
DESCRIPTION:I present two research threads applying deep learning to Monte
  Carlo and quantum mechanics.\n\nFirstly\, I discuss Fermionic neural netw
 orks and quantum Monte Carlo. This part of the talk will be largely based 
 on [1]. Since the paper is a few years old and has led to quite a bit of f
 ollow up work\, I will try to offer a perspective both on what it was like
  to get it working and also comment with the benefit of hindsight.\n\nSeco
 nd\, I will discuss a thread of work accelerating well established Monte C
 arlo sampling approaches with methods from machine learning. I will use sa
 mpling of lattice field theories as a motivating physical example. This pa
 rt of the talk will be based on [2] with some of [3] if there is time.\n\n
 [1] Ab initio solution of the many-electron Schrödinger equation with dee
 p neural networks\, David Pfau\, James S. Spencer\, Alexander G. D. G. Mat
 thews\, and W. M. C. Foulkes\,\nPhys. Rev. Research. 2020.\n[2] Continual 
 Repeated Annealed Flow Transport Monte Carlo\, \nAlexander G D G Matthews\
 , Michael Arbel\, Danilo Jimenez Rezende\, Arnaud Doucet\, International C
 onference on Machine Learning (ICML)\, 2022.\n[3] Score-based diffusion me
 ets Annealed Importance Sampling. \nArnaud Doucet\, Will Grathwohl\, Alexa
 nder G. D. G. Matthews & Heiko Strathmann\, Neural Information Processing 
 Systems (NeurIPS)\, 2022.
LOCATION:TCM Seminar Room
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