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 inference time, without any weight updates. ICON acts as a few-shot learner across forward and inverse problems for ODEs, PDEs, and mean-field control. I will […]
Events
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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)
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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, joint with Victor Ginzburg, which describes the coordinate ring of the group scheme of universal centralizers in terms of the root datum of G using Demazure (or divided […] |
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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, allowing pairwise comparisons to include strict preferences, ties, or incomparability. This perspective gives a common framework for median procedures and scoring methods, including several familiar […] |
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Topology, akin to geometry, delves into the study of the shapes and structures of mathematical spaces, ranging from simple surfaces to intricate collections of functions and objects. In recent years, […] |
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