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From ICON to GenICON: In-Context Operator Learning with Uncertainty Quantification (Siting Liu, UCR)
From ICON to GenICON: In-Context Operator Learning with Uncertainty Quantification (Siting Liu, UCR)
Abstract: I will introduce In-Context Operator Networks (ICON), a framework in which a single neural network learns solution operators for differential equations directly from a few prompted input-output examples at […] |
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One of the key objects used in Ngo's proof of the fundamental lemma is the group scheme of universal centralizers associated to a split reductive group G. In this talk, we'll discuss forthcoming work, […] |
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CCMS Colloquium invites you to a talk by Andrew Fiss (Michigan Technological University) Title: “Singing American Math: College Traditions from Book Burnings to Observatory Parties, 1880s-1920s" Abstract: “Singing Math” is a practice […]
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Abstract: Consider n independent, biased coins, each with a known probability of heads. Presented with an ordering of these coins, flip (i.e., toss) each coin once, in that order, until […] |
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Many aggregation problems ask us to turn individual judgments into a single collective outcome. In this talk, we model each voter’s input as a relation on a set of alternatives, […] |
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