Social Event at ICML 2024
We are hosting a social event with our sponsor, Together AI, on July 24 at 7PM. You can RSVP here!
Topics of Interest
Many challenging tasks for foundation models require synthesizing information over thousands to millions of individual pieces of data, which may take many forms, including images, text, audio, genomes, etc. Our workshop aims to convene researchers to address challenges in long-context foundation models, fostering discussions, developments, deployments, evaluation, and understanding of long-context foundation models across various AI disciplines. The topics of this workshop include (but not limited to):
- New modeling, training, and data strategies.
- Efficiency techniques for (long-context) foundation models.
- Evaluation and understanding of long-context models.
- Retrieval-augmented foundation models.
- Interdisciplinary applications of LCFMs.
Speakers
Albert Gu
Carnegie Mellon University
Mohit Iyyer
University of Massachusetts Amherst
Beidi Chen
Carnegie Mellon University
Panelists
Beidi Chen
Carnegie Mellon University
Angela Fan
Meta
Albert Gu
Carnegie Mellon University
Caglar Gulcehre
EPFL
Organizers
Weijia Shi
University of Washington
Amanda Bertsch
Carnegie Mellon University
Tri Dao
Together.AI, Princeton University
Danqi Chen
Princeton University
Graham Neubig
Carnegie Mellon University
Christopher Ré
Stanford University
