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SUMMARY:Yoshua Bengio (Université de Montréal)
DTSTART:20200723T190000Z
DTEND:20200723T203000Z
DTSTAMP:20260423T021057Z
UID:IASML/12
DESCRIPTION:Title: <a href="https://researchseminars.org/talk/IASML/12/">P
 riors for Semantic Variables</a>\nby Yoshua Bengio (Université de Montré
 al) as part of IAS Seminar Series on Theoretical Machine Learning\n\n\nAbs
 tract\nSome of the aspects of the world around us are captured in natural 
 language and refer to semantic high-level variables\, which often have a c
 ausal role (referring to agents\, objects\, and actions or intentions). Th
 ese high-level variables also seem to satisfy very peculiar characteristic
 s which low-level data (like images or sounds) do not share\, and it would
  be good to clarify these characteristics in the form of priors which can 
 guide the design of machine learning systems benefitting from these assump
 tions. Since these priors are not just about the joint distribution betwee
 n the semantic variables (e.g. it has a sparse factor graph corresponding 
 to a modular decomposition of knowledge) but also about how the distributi
 on changes (typically by causal interventions)\, this analysis may also he
 lp to build machine learning systems which can generalize better out-of-di
 stribution. Introducing such assumptions is necessary to even start having
  a theory about generalizing out-of-distribution. There are also fascinati
 ng connections between these priors and what is hypothesized about conscio
 us processing in the brain\, with conscious processing allowing us to reas
 on (i.e.\, perform chains of inferences about the past and the future\, as
  well as credit assignment) at the level of these high-level variables. Th
 is involves attention mechanisms and short-term memory to form a bottlenec
 k of information being broadcast around the brain between different parts 
 of it\, as we focus on different high-level variables and some of their in
 teractions. The presentation summarizes a few recent results using some of
  these ideas for discovering causal structure and modularizing recurrent n
 eural networks with attention mechanisms in order to obtain better out-of-
 distribution generalization and move deep learning towards capturing some 
 of the functions associated with conscious processing over high-level sema
 ntic variables.\n
LOCATION:https://researchseminars.org/talk/IASML/12/
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