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3 events,
Lecture in AI: Danqi Chen
Lecture in AI: Danqi Chen
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…
Continual Learning Working Group: Lea Duncker
Continual Learning Working Group: Lea Duncker
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…
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CTN Lab: Ashok Litwin-Kumar
CTN Lab: Ashok Litwin-Kumar
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 a handful of variables, like heading direction or retinotopy. We are framing the problem as learning a graph embedding, but I will also mention other…