Events
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Denise Cai Title: Dynamic neural ensembles support memory stability and flexibility across the lifetime Abstract: Creating stable memories is critical for survival. An animal relies on past learning to navigate its environment, avoid dangerous situations, and find needed resources. Because the environment is dynamic, stable memories must be updated with new information to enable responses… |
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Date and time: Monday, March 2, from 3–4 PM. Meeting Link: Upon request @[email protected] Speakers: Jorge Menendez, Research Scientist at CTRL-Labs, and Trung Le, postdoc in Prof. Chethan Pandarinath’s group. Title: A generic non-invasive neuromotor interface for human-computer interaction Since the advent of computing, humans have sought computer input technologies that are expressive, intuitive and… |
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Date: Wednesday, March 4, 2026 Time: 9:00 AM - 6:30 PM Location: Faculty Room, Low Memorial Library Address: 535 W. 116th Street, New York, NY 10027 - Visitor Information Overview How does a university learn and adapt as AI becomes woven into teaching, learning, and intellectual life? This program invites both celebration of innovative experimentation… |
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Title: Foundation models of the brain Abstract: You … your memories and ambitions, your sense of personal identity and free will, are in fact no more than the behavior of a vast assembly of nerve cells …’ Crick’s words capture the profound challenge of decrypting the neural code. This challenge has long been hindered by our limited… |
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Title: Are Vision-Language Models Checking or Looking? Abstract: Today’s AI vision systems are trained on vast amounts of data, yet it remains unclear whether they simply retrieve memorized answers or actively reason. We conjecture that hallucinations and limited creativity in these models stem from an over-reliance on superficial "checking" rather than active "looking." Checking retrieves the… |
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General website Title: Integrating Past and Present in Continual Learning Abstract: Continual learning aims to bridge the gap between typical human and machine-learning environments. The continual setting does not have separate training and testing phases, and instead models are evaluated online while learning novel concepts and tasks. The most capable current AI systems struggle to… |
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