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DTSTART;TZID=America/New_York:20230507T090000
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SUMMARY:Workshop on Global Categorical Symmetries
DESCRIPTION:The CMSA will be hosting a Workshop on Global Categorical Symmetries from May 7 – 12\, 2023 \nParticipation in the workshop is by invitation. \nPublic Lectures \nThere will be three lectures on Thursday\, May 11\, 2023\, which are open to the public.\nLocation:  Room G-10\, CMSA\, 20 Garden Street\, Cambridge MA 02138\nNote: The public lectures will be held in-person only. \n2:00 – 2:50 pm\nSpeaker: Kantaro Ohmori (U Tokyo )\nTitle: Fusion Surface Models: 2+1d Lattice Models from Higher Categories\nAbstract: Generalized symmetry in general dimensions is expected to be described by higher categories. Conversely\, one might expect that\, given a higher category with appropriate structures\, there exist models that admit the category as its symmetry. In this talk I will explain a construction of such 2+1d lattice models for fusion 2-categories defined by Douglas and Reutter\, generalizing the work of Aasen\, Fendley and Mong on anyon chains. The construction is by decorating a boundary of a topological Freed-Teleman-Moore sandwich into a non-topological boundary. In particular we can construct a family of candidate lattice systems for chiral topological orders. \n  \n3:00 – 3:50 pm\nSpeaker: David Jordan (Edinburgh)\nTitle: Langlands duality for 3-manifolds\nAbstract: Originating in number theory\, and permeating representation theory\, algebraic geometry\, and quantum field theory\, Langlands duality is a pattern of predictions relating pairs of mathematical objects which have no clear a priori mathematical relation. In this talk I’ll explain a new conjectural appearance of Langlands duality in the setting of 3-manifold topology\, I’ll give some evidence in the form of special cases\, and I’ll survey how the conjecture relates to both the arithmetic and geometric Langlands duality conjectures. \n3:50 – 4:30 pm\nTea/Snack Break \n4:30 – 5:30 pm\nSpeaker: Ken Intriligator (UCSD)\nColloquium\nTitle: QFT Aspects of Symmetry\nAbstract: Everything in the Universe\, including the photons that we see and the quarks and electrons in our bodies\, are actually ripples of quantum fields. Quantum field theory (QFT) is the underlying mathematical framework of Nature\, and in the case of electrons and photons it is the most precisely tested theory in science. Strongly coupled aspects\, e.g. the confinement of quarks and gluons at long distances\, remain challenging. QFT also describes condensed matter systems\, connects to string theory and quantum gravity\, and describes cosmology. Symmetry has deep and powerful realizations and implications throughout physics\, and this is especially so for the study of QFT. Symmetries play a helpful role in characterizing the phases of theories and their behavior under renormalization group flows (zooming out). Quantum field theory has also been an idea generating machine for mathematics\, and there has been increasingly fruitful synergy in both directions. We are currently exploring the symmetry-based interconnections between QFT and mathematics in our Simons Collaboration on Global Categorical Symmetry\, which is meeting here this week. I will try to provide an accessible\, colloquium-level introduction to aspects of symmetries and QFT\, both old and new.
URL:https://cmsa.fas.harvard.edu/event/globalcomputing23/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Workshop
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DTSTART;TZID=America/New_York:20230510T140000
DTEND;TZID=America/New_York:20230510T150000
DTSTAMP:20260405T013518
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SUMMARY:Modern Hopfield Networks for Novel Transformer Architectures
DESCRIPTION:New Technologies in Mathematics Seminar \nSpeaker: Dmitry Krotov\, IBM Research – Cambridge \nTitle: Modern Hopfield Networks for Novel Transformer Architectures \nAbstract: Modern Hopfield Networks or Dense Associative Memories are recurrent neural networks with fixed point attractor states that are described by an energy function. In contrast to conventional Hopfield Networks\, which were popular in the 1980s\, their modern versions have a very large memory storage capacity\, which makes them appealing tools for many problems in machine learning and cognitive and neurosciences. In this talk\, I will introduce an intuition and a mathematical formulation of this class of models and will give examples of problems in AI that can be tackled using these new ideas. Particularly\, I will introduce an architecture called Energy Transformer\, which replaces the conventional attention mechanism with a recurrent Dense Associative Memory model. I will explain the theoretical principles behind this architectural choice and show promising empirical results on challenging computer vision and graph network tasks.
URL:https://cmsa.fas.harvard.edu/event/nt-51023/
LOCATION:Virtual
CATEGORIES:New Technologies in Mathematics Seminar
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-NTM-Seminar-05.10.23.png
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