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SUMMARY:Model checking: Neural Termination Analysis - Daniel Kroening (Ama
 zon Web Services)
DTSTART:20220722T131500Z
DTEND:20220722T140000Z
UID:TALK176270@talks.cam.ac.uk
DESCRIPTION:We introduce a novel approach to the automated termination ana
 lysis of computer programs: we train neural networks to behave as ranking 
 functions. Ranking functions map program states to values that are bounded
  from below and decrease as the program runs. &nbsp\;The existence of a ra
 nking function proves that the program terminates. &nbsp\;We learn ranking
  functions from execution traces by training a neural network so that its 
 output decreases along the sampled executions\; then\, we use symbolic rea
 soning to formally verify that it generalises to all possible executions. 
 &nbsp\;We demonstrate that\, thanks to the ability of neural networks to r
 epresent highly complex functions\, our method succeeds over programs that
  are beyond the reach of state-of-the-art tools. &nbsp\;This includes prog
 rams that use loop guards with disjunctions and programs that exhibit nonl
 inear behaviour.&nbsp\;
LOCATION:Seminar Room 2\, Newton Institute
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