Mamba: Linear-Time Sequence Modeling with Selective State Spaces
Mamba investigates selective state-space models as an alternative approach to sequence modeling with efficient scaling.
Paper & contextModeling long sequences with structured state spaces.
Gu studies efficient models for long sequences. This selection centers on structured state spaces and Mamba, then follows research on memory, recurrence, and alternatives to conventional attention-based sequence processing.
10 papers
Mamba investigates selective state-space models as an alternative approach to sequence modeling with efficient scaling.
Paper & contextS4 makes structured state-space models efficient enough to learn from long sequences.
Paper & contextSelected research in efficient ai. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in efficient ai. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in efficient ai. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in efficient ai. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in efficient ai. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in efficient ai. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in efficient ai. Read the full paper, including the methods, experiments, and reported results.
Paper & contextSelected research in efficient ai. Read the full paper, including the methods, experiments, and reported results.
Paper & contextAn independent editorial profile. Inclusion does not imply Council membership or endorsement. Research is collaborative; coauthorship does not imply sole credit.
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