Title: Wandering Within a World A discussion on Wandering Within a World: Online Contextualized Few-Shot Learning, this 2021 paper by our very own Rich Zemel leverages contextual information in a continually changing environment to improve model performance in realistic settings. Zoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1

Title: TBD Abstract: TBD

Title: Statistical mechanics of transfer learning in the proportional limit Abstract: Transfer learning (TL) is a well-established machine learning technique to boost the generalization performance on a specific (target) task using information gained from a related (source) task, and it crucially depends on the ability of a network to learn useful features. I will present…

Title: Training Language Models in Academic: Research Questions and Opportunities Abstract: Large language models have emerged as transformative tools in artificial intelligence, demonstrating unprecedented capabilities in understanding and generating human language. While these models have achieved remarkable performance across a wide range of benchmarks and enabled groundbreaking applications, their development has been predominantly concentrated within…

Title: TBD Abstract: TBD

Title: Task-dependent low-dimensional population dynamics for robustness and learning Abstract: Biological systems face dynamic environments that require flexibly deploying learned skills and continual learning of new tasks. It is not well understood how these systems balance the tension between flexibility for learning and robustness for memory of previous behaviors. Neural activity underlying single, highly controlled…

Title: Searching for symmetries in connectome data Abstract: I will talk about work with Haozhe Shan on identifying structure in connectome data that suggests a cell type encodes one or…

Title: Neuromorphic Computing and the Significance of Medium Dependence   Abstract: The increasingly prohibitive cost of energy demanded by large artificial neural networks (ANNs) is giving new impetus to research and…

Title: Building foundation models for neuroscience Abstract: Current methodologies for recording brain activity often provide narrow views of the brain's function. This fragmentation of datasets has hampered the development of…

Title: Mapping bioelectrical signals, from dendrites to circuits Abstract: Neuronal dendrites are excitable, but what are these excitations for?  Are dendritic excitations involved in integration?  Or in mediating back-propagation?  What are…

Title: Disentangling the Roles of Distinct Cell Classes with Cell-Type Dynamical Systems   Abstract: Latent dynamical systems have been widely used to characterize the dynamics of neural population activity in the…

Title: Discovering Symbolic Cognitive Models from Human and Animal Behavior with CogFunSearch Abstract: A key goal of cognitive science is to discover mathematical models that describe how the brain implements cognitive processes.…