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SUMMARY:Alhussein Fawzi (DeepMind)
DTSTART:20230119T170000Z
DTEND:20230119T180000Z
DTSTAMP:20260423T003239Z
UID:MPML/95
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/MPML/95/">Di
 scovering faster matrix multiplication algorithms with deep reinforcement 
 learning</a>\nby Alhussein Fawzi (DeepMind) as part of Mathematics\, Physi
 cs and Machine Learning (IST\, Lisbon)\n\n\nAbstract\nImproving the effici
 ency of algorithms for fundamental computational tasks such as matrix mult
 iplication can have widespread impact\, as it affects the overall speed of
  a large amount of computations. The automatic discovery of algorithms usi
 ng machine learning offers the prospect of reaching beyond human intuition
  and outperforming the current best human-designed algorithms. In this tal
 k I'll present AlphaTensor\, our reinforcement learning agent based on Alp
 haZero for discovering efficient and provably correct algorithms for the m
 ultiplication of arbitrary matrices. AlphaTensor discovered algorithms tha
 t outperform the state-of-the-art complexity for many matrix sizes. Partic
 ularly relevant is the case of 4 × 4 matrices in a finite field\, where A
 lphaTensor's algorithm improves on Strassen's two-level algorithm for the 
 first time since its discovery 50 years ago. I'll present our problem form
 ulation as a single-player game\, the key ingredients that enable tackling
  such difficult mathematical problems using reinforcement learning\, and t
 he flexibility of the AlphaTensor framework.\n
LOCATION:https://researchseminars.org/talk/MPML/95/
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