Dr. Richard Lange
Zuckerman Institute - L3-079 3227 Broadway, New York, NY, United StatesTitle: "What Bayes can and cannot tell us about the neuroscience of vision" Nikolaus Kriegeskorte's Group is hosting Dr.Richard Lange, Assistant Professor in the Department of Computer Science at Rochester…
Continual Learning Working Group Talk
CEPSR 620 Schapiro 530 W. 120th StTitle: Continual learning, machine self-reference, and the problem of problem-awareness Abstract: Continual learning (CL) without forgetting has been a long-standing problem in machine learning with neural networks. Here I will bring a…
CTN Claudia Clopath
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: Feedback-based motor control can guide plasticity and drive rapid learning Abstract: Animals use afferent feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable…
CTN: Sebastian Seung
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: Insights into vision from interpreting a neuronal wiring diagram Host: Marcus Triplett Abstract: In 2023, the FlyWire Consortium released the neuronal wiring diagram of an adult fly brain. This contains as a…
CTN: Stephanie Palmer
Zuckerman Institute - L5-084 3227 Broadway, New York, NY, United StatesTitle: How behavioral and evolutionary constraints sculpt early visual processing Abstract: Biological systems must selectively encode partial information about the environment, as dictated by the capacity constraints at work in all living…
Continual Learning Working Group: Kick Off
CEPSR 620 Schapiro 530 W. 120th StSpeaker: Mengye Ren Title: Lifelong and Human-like Learning in Foundation Models Abstract: Real-world agents, including humans, learn from online, lifelong experiences. However, today’s foundation models primarily acquire knowledge through offline, iid learning, while…
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.…
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…
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…
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
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
Continual Learning Working Group: Amogh Inamdar
CSB 488Title: Taskonomy: Disentangling Task Transfer Learning Abstract: TBD Link: http://taskonomy.stanford.edu/taskonomy_CVPR2018.pdf