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IAP Research (United States)

United States

8 papers here350 citations across 8
fields
  • astro-ph.IM2
  • astro-ph1
  • astro-ph.GA1
  • cs.CV1
  • cs.LG1
  • quant-ph1
  • stat.ML1
ROR 018k5nw16OpenAlex

affiliations via OpenAlex

output
20062026
most citedTransformers are RNNs: Fast Autoregressive Transformers with Linear Attention

310 citations

researchers with a paper here
  • A. Argan1 · h 41
  • A. Bonnamy1 · h 13
  • A. Bulgarelli1 · h 48
  • A. Coleiro1 · h 7
  • A. Correia1 · h 9
  • A. D’Aì1 · h 28
  • A. Decourchelle1 · h 30
  • A. Diaferio1 · h 42
  • A. Finoguenov1 · h 78
  • A. Goldwurm1 · h 50
  • A. Janiuk1 · h 27
  • A. Krone-Martins1 · h 35
collaborating institutions
  • Idiap Research InstituteCH3 papers
  • Centre National de la Recherche ScientifiqueFR2 papers
  • École Polytechnique Fédérale de LausanneCH2 papers
  • Institut d'Astrophysique de ParisFR2 papers
  • Leiden UniversityNL2 papers
  • Osservatorio Astrofisico di TorinoIT2 papers
  • Torino e-districtIT2 papers
  • University of MichiganUS2 papers
  • Aalto UniversityFI1 paper
  • Academy of Performing Arts in PragueCZ1 paper
  • Agence Parisienne du ClimatFR1 paper
  • Agnes Scott CollegeUS1 paper
Showing cs.LGShow all

1 paper · 1 filter

cs.LG2020★ 310 cited

Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention

Angelos Katharopoulos, Apoorv Vyas, Nikolaos Pappas +1

Transformers achieve remarkable performance in several tasks but due to their quadratic complexity, with respect to the input's length, they are prohibitively slow for very long se…

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