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DTSTAMP:20240130T121551
DTSTART;TZID=America/Detroit:20250418T100000
DTEND;TZID=America/Detroit:20250418T200000
SUMMARY:Exhibition:Unsettling Histories: Legacies of Slavery and Colonialism
DESCRIPTION:Organized as a response to the Museum’s recent acquisition of Titus Kaphar’s Flay (James Madison)\, this upcoming reinstallation of one of our most prominent gallery spaces forces us to grapple with our collection of European and American art\, 1650-1850.\n \nIn recent times\, growing public awareness of the continued reverberations of the legacy of slavery and colonization has challenged museums to examine the uncomfortable histories contained in our collections\, and challenged the public to probe the choices we make about those stories. Choices about which artists you see in our galleries\, choices about what relevant facts we share about the works\, and choices about what - out of an infinite number of options - we don’t say about them.\n \nPieces in this exhibition were made at a time when the world came to be shaped by the ideologies of colonial expansion and Western domination. And yet\, that history and the stories of those marginalized do not readily appear in the still lives and portraits on display here. By grappling with what is visible and what remains hidden\, we are forced to examine whose stories and histories are prioritized and why.  \n \nIn this online exhibition\, you can explore our efforts to deeply question the Museum’s collection and our own past complicity in favoring colonial voices. In the Museum gallery\, which will open in early 2021\, you’ll be able to experience the changes we’re making to the physical space to highlight a more honest version of European and American history. \n \nBy challenging our own practice\, and continuing to add to what we know and what we write about the works we display\, UMMA tells a more complex and more complete story of this nation - one that unsettles\, and fails to settle for\, simple narratives. \n \n“Invisible things are not necessarily ‘not there’.... Certain absences are so stressed\, so ornate\, so planned\, they call attention to themselves\; arrest us with intentionality and purpose\, like neighborhoods that are defined by the population held away from them.” \n \n— Toni Morrison\n\nLead support for Unsettling Histories: Legacies of Slavery and Colonialism is provided by the University of Michigan Office of the Provost\, the U-M Arts Initiative\, and the Susan and Richard Gutow Endowed Fund.\n 
UID:84303-21621573@events.umich.edu
URL:https://events.umich.edu/event/84303
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Art,UMMA,Museum,History,Exhibition,European
LOCATION:Museum of Art - European and American Decorative Art
CONTACT:
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DTSTAMP:20250416T130417
DTSTART;TZID=America/Detroit:20250418T103000
DTEND;TZID=America/Detroit:20250418T233000
SUMMARY:Workshop / Seminar:Bridging the theory-practice gap in machine learning: new results in sampling and optimization
DESCRIPTION:Meteoric progress in machine learning over the last decade has outpaced our foundational understanding\, limiting our ability to harness the technology effectively in applications that require performance guarantees\, and inviting the development of theory to enable such applications. At the heart of this progress is a highly productive connection to gradient-based optimization\, the efficacy of which we are so far unable to fully explain. Motivated by this issue\, in the first part of the talk\, I will briefly describe an interpretable and computationally efficient adaptive step-size method for gradient-based optimization that relies on ideas from the numerical analysis of ordinary differential equations. I will show how this method connects studies of popular optimizers for machine learning in continuous time—where they are often more amenable to analysis—with their practical discrete-time implementations.\n\nSurprising recent developments in machine learning include the ability to generate---or sample---perceptual data such as natural images and language. Markov Chain Monte Carlo (MCMC) algorithms have long provided a generic recipe for sampling from probability distributions of interest. The Gibbs sampler is a specific limit of an MCMC algorithm and is the natural choice for sampling from a simple model of image patches. In the bulk of this talk\, I will focus on a new mixing time bound for Gibbs sampling from well-conditioned log-concave distributions. I will outline the proof of the bound and place it within the context of ongoing efforts in the broader community to understand the efficacy of diffusion-based image generation methods. Time permitting\, I will discuss potential applications of these efforts to problems in cosmology and biophysics.
UID:135076-21876044@events.umich.edu
URL:https://events.umich.edu/event/135076
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Mathematics
LOCATION:East Hall - 3866
CONTACT:
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