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DTSTART:20230101T000000
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DTSTART;TZID=UTC:20240502T185700
DTEND;TZID=UTC:20240502T185700
DTSTAMP:20240502T225819Z
CREATED:20240423T172750Z
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UID:814-1714676220-1714676220@arni-institute.org
SUMMARY:Continual Learning Working Group
DESCRIPTION:Weekly Meeting Group Discussion: Lifelong and Human-like Learning in Foundation Models \nSpeaker: Mengye Ren (New York University)\nAssistant Professor\nDepartment of Computer Science\nCourant Institute of Mathematical Sciences\nCenter for Data Science (joint)\nNew York University \nAbstract: Real-world agents\, including humans\, learn from online\, lifelong experiences. However\, today’s foundation models primarily acquire knowledge through offline\, iid learning\, while relying on in-context learning for most online adaptation. It is crucial to equip foundation models with lifelong and human-like learning abilities to enable more flexible use of AI in real-world applications. In this talk\, I will discuss recent works exploring interesting phenomena in foundation models when learning in online\, structured environments. Notably\, foundation models exhibit anticipatory and semantically-aware memorization and forgetting behaviors. Furthermore\, I will introduce a new method that combines pretraining and meta-learning for learning and consolidating new concepts in large language models. This approach has the potential to lead to future foundation models with incremental consolidation and abstraction capabilities.
URL:https://arni-institute.org/event/continual-learning-working-group-9/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
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