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DTSTART;TZID=America/New_York:20241206T110000
DTEND;TZID=America/New_York:20241206T120000
DTSTAMP:20260426T181613
CREATED:20241210T193448Z
LAST-MODIFIED:20241210T193448Z
UID:1245-1733482800-1733486400@arni-institute.org
SUMMARY:Lecture in AI: Danqi Chen
DESCRIPTION:Title: Training Language Models in Academic: Research Questions and Opportunities \nAbstract: 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 large technology companies due to substantial computational and proprietary data requirements. In this talk\, I will present a vision for how academic research can play a critical role in advancing the open language model ecosystem\, particularly by developing smaller yet highly capable models and advancing our fundamental understanding of training practices. Drawing from our research group’s recent projects\, I will examine key research questions and challenges in both pre-training and post-training stages. Our work spans developing small language models (Sheared LLaMA; 1-3B parameters)\, the state-of-the-art <10B model on Chatbot Arena (gemma-2-SimPO)\, and long-context models supporting up to 512K tokens (ProLong). These examples illustrate how academic research can push the boundaries of model efficiency\, capability\, and scalability. I will conclude by exploring future directions and highlighting opportunities to shape the development of more accessible and powerful language models.
URL:https://arni-institute.org/event/lecture-in-ai-danqi-chen/
LOCATION:Davis Auditorium\, 530 W 120th St\, New York\, NY 10027\, New York\, NY\, 10027
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DTSTART;TZID=America/New_York:20241206T110000
DTEND;TZID=America/New_York:20241206T130000
DTSTAMP:20260426T181613
CREATED:20241202T175520Z
LAST-MODIFIED:20241202T175520Z
UID:1211-1733482800-1733490000@arni-institute.org
SUMMARY:CTN Seminar: Andrew Leifer
DESCRIPTION:Title: TBD \nAbstract: TBD
URL:https://arni-institute.org/event/ctn-seminar-andrew-leifer/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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DTSTART;TZID=America/New_York:20241206T140000
DTEND;TZID=America/New_York:20241206T150000
DTSTAMP:20260426T181613
CREATED:20241111T160235Z
LAST-MODIFIED:20241204T160339Z
UID:1159-1733493600-1733497200@arni-institute.org
SUMMARY:Continual Learning Working Group: Lea Duncker
DESCRIPTION:Title: Task-dependent low-dimensional population dynamics for robustness and learning \nAbstract: 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 experimental tasks has repeatedly been observed to exhibit low-dimensional structure. However\, it is unclear how this organization arises and is maintained throughout learning\, and how it might differ when networks are exposed to multiple tasks. In this talk\, I will present work on a continual learning rule designed to minimize interference between sequentially learned tasks in recurrent networks. The learning rule preserves network dynamics within activity-defined low-dimensional subspaces used for previously learned tasks. It encourages recurrent dynamics associated with interfering tasks to explore orthogonal subspaces. Employing a set of tasks used in neuroscience\, I will show that this approach can successfully eliminate catastrophic interference\, while allowing for reuse of similar low-dimensional dynamics across similar tasks. This possibility for shared computation allows for faster learning during sequential training. Finally\, I will highlight limitations of this approach in fully exploiting task-similarity for optimal re-use of previously learned solutions\, and outline new work we are starting in my group now to address this. \nZoom Link: https://columbiauniversity.zoom.us/j/97176853843?pwd=VLZdh6yqHBcOQhdf816lkN5ByIpIsF.1
URL:https://arni-institute.org/event/continual-learning-working-group-lea-duncker/
LOCATION:CEPSR 620\, Schapiro 530 W. 120th St
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20241209T113000
DTEND;TZID=America/New_York:20241209T130000
DTSTAMP:20260426T181613
CREATED:20241202T205723Z
LAST-MODIFIED:20241202T205723Z
UID:1214-1733743800-1733749200@arni-institute.org
SUMMARY:CTN Lab: Ashok Litwin-Kumar
DESCRIPTION:Title: Searching for symmetries in connectome data\n\nAbstract: 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 things we have considered which\, at least for me\, were educational. The project is at an early stage\, so we would welcome suggestions and ideas.
URL:https://arni-institute.org/event/ctn-lab-ashok-litwin-kumar/
LOCATION:Zuckerman Institute – L5-084\, 3227 Broadway\, New York\, NY\, United States
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