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        "event_title":"Alum Panel: Behind the Screens \u2014 Breaking Into Film, TV, and Media",
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        "event_title":"King Talks Information Session",
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        "event_title":"Normalization effects on deep neural networks and deep learning for scientific problems.",
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        "description":"We study the effect of normalization on the layers of deep neural networks. A given layer i with N_{i} hidden units is normalized by 1\/N_{i}^{\u03b3_{i}} with \u03b3_{i}\u2208[1\/2,1]. We study the effect of the choice of the \u03b3_{i} on the statistical behavior of the neural network\u2019s output (such as variance) as well as on the test accuracy and generalization properties of the architecture. We find that in terms of variance of the neural network\u2019s output and test accuracy the best choice is to choose the \u03b3_{i}\u2019s to be equal to one, which is the mean-field scaling. We also find that this is particularly true for the outer layer, in that the neural network\u2019s behavior is more sensitive in the scaling of the outer layer as opposed to the scaling of the inner layers. The mechanism for the mathematical analysis is an asymptotic expansion for the neural network\u2019s output. An important practical consequence of the analysis is that it provides a systematic and mathematically informed way to choose the learning rate hyperparameters. Such a choice guarantees that the neural network behaves in a statistically robust way as the number of hidden units N_{i} grow. Time permitting, I will discuss applications of these ideas to design of deep learning algorithms for scientific problems including solving high dimensional partial differential equations (PDEs), closure of PDE models and reinforcement learning with applications to financial engineering, turbulence and more.",
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        "date_start":"2025-10-15",
        "date_end":"2025-10-15",
        "time_start":"17:00:00",
        "time_end":"18:30:00",
        "time_zone":"America\/Detroit",
        "event_title":"Fright Night: Student Org Leader Enrichment",
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        "combined_title":"Fright Night: Student Org Leader Enrichment",
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        "event_type":"Workshop \/ Seminar",
        "event_type_id":"21",
        "description":"This event is intended to gather leaders from all student organizations to learn about the scariest thing to navigate: funding resources. After a short presentation to equip you with the tools to tackle this monster, you'll be able to use SPH craft supplies to design your own unique org mascot and dress it (or yourself) up! You'll also get to play a spooky-themed game of Bingo to help you get to know your peers in leadership while you enjoy light snacks. This is intended to be a safe space to talk through anything that might be on your mind, to talk about what excites you and scares you about the year ahead.\u00a0",
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        "location_name":"1680 SPH I (Paul B. Cornely Room)",
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        "tags":["Sessions"],
        "website":"https:\/\/sessions.studentlife.umich.edu\/p\/track\/15271",
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