NEST is a nested transformer for sequences of multisets that uses masked set modeling to learn improved set-level representations from hierarchical event streams like EHRs.
An exploration of hierarchical attention transformers for efficient long document classification
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A two-level overlapping Schwarz domain decomposition constructs a hierarchical attention operator that trains faster and approximates the inverse of a discretized 1D diffusion operator more accurately than global low-rank attention while using fewer parameters.
The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.
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NEST: Nested Event Stream Transformer for Sequences of Multisets
NEST is a nested transformer for sequences of multisets that uses masked set modeling to learn improved set-level representations from hierarchical event streams like EHRs.
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Hierarchical Attention via Domain Decomposition
A two-level overlapping Schwarz domain decomposition constructs a hierarchical attention operator that trains faster and approximates the inverse of a discretized 1D diffusion operator more accurately than global low-rank attention while using fewer parameters.