Events
Calendar of 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… |
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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.… |
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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… |
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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… |
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Title: Constraints of efficient neural computation Abstract: Neural systems adapt to the statistical structure of the environment to support behavior. While it is generally recognized that such adaptation is subject… |
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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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Continuation of prior meetings. Zoom: Upon request @[email protected] |
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Time: 30th March. 3pm EST Title: Large scale models for spatiotemporal data. Speaker: Josue Ortega Caro https://josueortc.github.io/ Abstract: Spatiotemporal and multimodal datasets contain structured variability distributed across space, time, and measurement modality, motivating modeling approaches that can learn representations directly from large-scale data. Inspired by video foundational models, we study how the masked autoencoder training objective can… |
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