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DTSTART;TZID=America/New_York:20221102T124500
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DTSTAMP:20240121T174258Z
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UID:10001270-1667393100-1667396700@cmsa.fas.harvard.edu
SUMMARY:Doping and inverting Mott insulators on semiconductor moire superlattices
DESCRIPTION:Speaker: Liang Fu (MIT) \n\n\nTitle: Doping and inverting Mott insulators on semiconductor moire superlattices \nAbstract: Semiconductor bilayer heterostructures provide a remarkable platform for simulating Hubbard models on an emergent lattice defined by moire potential minima. As a hallmark of Hubbard model physics\, the Mott insulator state with local magnetic moments has been observed at half filling of moire band. In this talk\, I will describe new phases of matter that grow out of the canonical 120-degree antiferromagnetic Mott insulator on the triangular lattice. First\, in an intermediate range of magnetic fields\, doping this Mott insulator gives rise to a dilute gas of spin polarons\, which form a pseudogap metal. Second\, the application of an electric field between the two layers can invert the many-body gap of a charge-transfer Mott insulator\, resulting in a continuous phase transition to a quantum anomalous Hall insulator with a chiral spin structure. Experimental results will be discussed and compared with theoretical predictions.
URL:https://cmsa.fas.harvard.edu/event/collquium-11222/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Colloquium
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Colloquium-11.02.22.png
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DTSTART;TZID=America/New_York:20221116T123000
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DTSTAMP:20240214T112838Z
CREATED:20230817T174642Z
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UID:10001271-1668601800-1668605400@cmsa.fas.harvard.edu
SUMMARY:Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks
DESCRIPTION:Colloquium \nSpeaker: Hidenori Tanaka (NTT Research at Harvard) \nTitle: Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks \nAbstract: In nature\, symmetry governs regularities\, while symmetry breaking brings texture. In artificial neural networks\, symmetry has been a central design principle\, but the role of symmetry breaking is not well understood. Here\, we develop a Lagrangian formulation to study the geometry of learning dynamics in neural networks and reveal a key mechanism of explicit symmetry breaking behind the efficiency and stability of modern neural networks. Then\, we generalize Noether’s theorem known in physics to describe a unique symmetry breaking mechanism in learning and derive the resulting motion of the Noether charge: Noether’s Learning Dynamics (NLD). Finally\, we apply NLD to neural networks with normalization layers and discuss practical insights. Overall\, through the lens of Lagrangian mechanics\, we have established a theoretical foundation to discover geometric design principles for the learning dynamics of neural networks.
URL:https://cmsa.fas.harvard.edu/event/collquium-111622/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Colloquium
ATTACH;FMTTYPE=image/png:https://cmsa.fas.harvard.edu/media/CMSA-Colloquium-11.16.22-2.png
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