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DTSTART;TZID=Europe/Stockholm:20250617T153000
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UID:submissions.pasc-conference.org_PASC25_sess129_msa196@linklings.com
SUMMARY:TADASHI: Enabling ML with Correct Code Transformations
DESCRIPTION:Emil Vatai and Aleksandr Drozd (RIKEN); Ivan R. Ivanov (Instit
 ute of Science Tokyo, RIKEN); Joao E. Batista (RIKEN); Yinghao Ren (SenseT
 ime Research and PowerTensors.AI); and Mohamed Wahib (RIKEN)\n\nAs the lan
 dscape of machine learning (ML) continues to evolve, the integration of ge
 nerative AI has become a focal point for automating code generation. While
  it is perfectly suitable to generate text for humans such as the abstract
  you're reading, this approach often falls short in ensuring the correctne
 ss of the generated code, leading to potential pitfalls in robust ML appli
 cations. Recent cases exemplify this challenge, where the use of AI to acc
 elerate coding processes resulted in faster but incorrect code, something 
 unacceptable in scientific computing. In response to this critical need, w
 e introduce TADASHI—a novel library designed to bridge the gap between spe
 ed and correctness in code transformations. TADASHI offers a user-friendly
  Python interface that can seamlessly integrate into existing ML scripts. 
 It empowers developers by not only expediting code modifications but also 
 enforcing rigorous correctness checks on these transformations. By ensurin
 g that code remains valid and reliable, TADASHI enhances the stability of 
 ML workflows and fosters confidence in automated processes. Join us as we 
 delve into TADASHI's capabilities, showcasing its potential to revolutioni
 ze the code transformation landscape in ML, ensuring that efficiency does 
 not compromise correctness.\n\nDomain: Computational Methods and Applied M
 athematics\n\nSession Chair: Florina Ciorba (University of Basel)\n\n
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