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DTSTAMP:20250910T135655
DTSTART;TZID=America/Detroit:20251010T100000
DTEND;TZID=America/Detroit:20251010T110000
SUMMARY:Workshop / Seminar:Statistics Department Seminar Series: Qing Qu\, Assistant Professor\, Electrical Engineering and Computer Science\, University of Michigan
DESCRIPTION:Abstract: Recent empirical studies have shown that diffusion models possess a unique reproducibility property\, transiting from memorization to generalization as the number of training samples increases. This demonstrates that diffusion models can effectively learn image distributions and generate new samples. Remarkably\, these models achieve this even with a small number of training samples\, despite the challenge of large image dimensions\, effectively circumventing the curse of dimensionality. In this work\, we provide theoretical insights into this phenomenon by leveraging two key empirical observations: (i) the low intrinsic dimensionality of image datasets and (ii) the low-rank property of the denoising autoencoder in trained diffusion models. With these setups\, we rigorously demonstrate that optimizing the training loss of diffusion models is equivalent to solving the canonical subspace clustering problem across the training samples. This insight has practical implications for training and controlling diffusion models. Specifically\, it enables us to precisely characterize the minimal number of samples necessary for accurately learning the low-rank data support\, shedding light on the phase transition from memorization to generalization. Additionally\, we empirically establish a correspondence between the subspaces and the semantic representations of image data\, which enables one-step\, transferrable\, efficient image editing. Moreover\, our results have profound practical implications for training efficiency and model safety\, and they also open up numerous intriguing theoretical questions for future research.
UID:139186-21885015@events.umich.edu
URL:https://events.umich.edu/event/139186
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:seminar
LOCATION:West Hall - 340
CONTACT:
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BEGIN:VEVENT
DTSTAMP:20250821T100218
DTSTART;TZID=America/Detroit:20251010T100000
DTEND;TZID=America/Detroit:20251010T120000
SUMMARY:Social / Informal Gathering:Write with ME!
DESCRIPTION:Working on an abstract? Polishing up your resume? Writing a paper or dissertation?\n\nJoin us for our new Mechanical Engineering Department writing group\, “Write with ME!”\n\nAll ME undergrads\, grads\, postdocs\, faculty\, and staff are welcome to join us for any of their writing needs.\n\nCommunity & support\nConnect with peers\, share your writing\, exchange feedback\, and brainstorm solutions to writing challenges.\n\nAccountability & consistency\nSharpen your writing skills and develop positive\, consistent writing routines. Learn from other members of the ME department!\n\nFood & flexibility\nNo need to attend every week! Drop in at any time\, and leave at any time. Light snacks\, coffee\, and tea will be available.\n\nWeekly on Fridays\, starting September 12\n2636 G.G.B\n10 am – 12 pm
UID:137880-21880950@events.umich.edu
URL:https://events.umich.edu/event/137880
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Faculty,Graduate Students,Mechanical Engineering,Postdoctoral Research Fellows,Staff,Undergraduate Students,Writing
LOCATION:GG Brown Laboratory - 2636
CONTACT:
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