Title: 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…

Speaker: 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…

NSF 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.…

Title: 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…

Speaker: 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…

Title: Bounded optimality: A cognitive perspective on neural computation with resource limitations

Title: Neuronal energy consumption: basic measures and trade-offs, and their effects on efficiency Zoom: https://columbiauniversity.zoom.us/j/98299154214?pwd=1J3J0lEpF6XdqHkHy02c7LuD6xUWx2.1

Title: Taskonomy: Disentangling Task Transfer Learning Abstract: TBD  Link: http://taskonomy.stanford.edu/taskonomy_CVPR2018.pdf

Title: 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…

Title: 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

Title: 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…

Title: 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