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DTSTART;TZID=America/New_York:20240220T153000
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CREATED:20240209T133752Z
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SUMMARY:CIS Seminar: "Towards Flexible\, Scalable\, and Knowledgeable Generative Intelligence"
DESCRIPTION:From language modeling to 3D vision\, generative AI has revolutionized nearly every aspect of machine learning. In this talk\, I will examine the limitations of the foundation behind many generative AI techniques–autoregressive models. Despite their impressive successes\, these token-by-token models face various challenges\, including 1). non-flexible computation during generation\, 2). lack of rich inner structures for scalable modeling\, and 3). limited understanding of the real world. \nTo address these three issues\, I propose to strategically predict “latents” for the design of new generative models\, where latents refer to the model’s intermediate representations during the generation process. First\, I will demonstrate how integrating latents allows flexible architecture designs to enhance both efficiency and adaptability \,such as in the first non-autoregressive model for sequence generation. Next\, I will show how to use latents to incorporate useful data structures for improved model scalability\, especially in high-resolution images and videos. Moreover\, I will demonstrate how to use latents to infuse world knowledge such as 3D for tasks like consistent view synthesis. Throughout the talk\, I will cover various modalities\, including text\, images\, and 3D. Finally\, I will conclude with a discussion about the prevailing challenges and envision future paths that could lead to more flexible\, scalable \,a nd knowledgeable next-generation generative models.
URL:https://seasevents.nmsdev7.com/event/cis-seminar-towards-flexible-scalable-and-knowledgeable-generative-intelligence/
LOCATION:Wu and Chen Auditorium (Room 101)\, Levine Hall\, 3330 Walnut Street\, Philadelphia\, PA\, 19104\, United States
ORGANIZER;CN="Computer and Information Science":MAILTO:cherylh@cis.upenn.edu
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