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ARNI NSF Site Visit
Innovation Hub Tang Family Hall - 2276 12TH AVENUE – FLOOR 02NSF Site Visit - The NSF team will evaluate the progress and achievements of ARNI’s projects to date and provide recommendations to steer future directions and funding for the project. If you are interested in learning more about ARNI over-all, join this Zoom link from 9am to 12pm or 2pm to 4:30pm.
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CTN: Eva Dyer
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: Large-scale pretraining on neural data allows for transfer across individuals, tasks and species Abstract: As neuroscience datasets grow in size and complexity, integrating diverse data sources to achieve a comprehensive understanding…
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Continual Learning Working Group: Haozhe Shan
CEPSR 620 Schapiro 530 W. 120th StSpeaker: Haozhe Shan Title: A theory of continual learning in deep neural networks: task relations, network architecture and learning procedure Abstract: Imagine listening to this talk and afterwards forgetting everything else…
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Multi-resource-cost Optimization for Neural Networks Models Working Group (NNMS): Tom Griffiths
Zuckerman Institute - L3-079 3227 Broadway, New York, NY, United StatesTitle: Bounded optimality: A cognitive perspective on neural computation with resource limitations
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Multi-resource-cost Optimization for Neural Networks Models Working Group (NNMS): Simon Laughlin
Zuckerman Institute - L3-079 3227 Broadway, New York, NY, United StatesTitle: Neuronal energy consumption: basic measures and trade-offs, and their effects on efficiency Zoom: https://columbiauniversity.zoom.us/j/98299154214?pwd=1J3J0lEpF6XdqHkHy02c7LuD6xUWx2.1
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Continual Learning Working Group: Amogh Inamdar
CSB 488Title: Taskonomy: Disentangling Task Transfer Learning Abstract: TBD Link: http://taskonomy.stanford.edu/taskonomy_CVPR2018.pdf
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CTN: Brenden Lake
Zuckerman Institute - L3-079 3227 Broadway, New York, NY, United StatesTitle: Meta-learning for more powerful behavioral modeling Abstract: Two modeling paradigms have historically been in tension: Bayesian models provide an elegant way to incorporate prior knowledge, but they make simplifying and constraining…
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Continual Learning Working Group: Lindsay Smith
CEPSR 620 Schapiro 530 W. 120th StTitle: A Practitioner’s Guide to Continual Multimodal Pretraining Reading: https://arxiv.org/pdf/2408.1447 Zoom: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1
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CTN: Benjamin Grewe
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: Target Learning rather than Backpropagation Explains Learning in the Mammalian Neocortex Abstract: Modern computational neuroscience presents two competing hypotheses for hierarchical learning in the neocortex: (1) deep learning-inspired approximations of the…
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Continual Learning Working Group: Yasaman Mahdaviyeh
CEPSR 620 Schapiro 530 W. 120th StTitle: Meta Continual Learning Revisited: Implicitly Enhancing Online Hessian Approximation via Variance Reduction Reading: https://openreview.net/pdf?id=TpD2aG1h0D Zoom: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1
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ARNI Annual Retreat
Faculty House 64 Morningside DrGeneral Agenda October 21st, Day 1 from 8:45am to 5pm Breakfast and Lunch Provided Opening 3 Keynote Speakers from ARNI Faculty Research Brainstorming and Discussions Project/Student Poster Session Education and…
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CTN: Tatiana Engel
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: Unifying neural population dynamics, manifold geometry, and circuit structure. Abstract: Single neurons show complex, heterogeneous responses during cognitive tasks, often forming low-dimensional manifolds in the population state space. Consequently, it is…
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