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  • [1706. 03762] Attention Is All You Need - arXiv. org
    The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration The best performing models also connect the encoder and decoder through an attention mechanism We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely
  • Attention Is All You Need
    Provided proper attribution is provided, Google hereby grants permission to reproduce the tables and figures in this paper solely for use in journalistic or scholarly works
  • Attention Is All You Need - arXiv. org
    Similarly, self-attention layers in the decoder allow each position in the decoder to attend to all positions in the decoder up to and including that position We need to prevent leftward information flow in the decoder to preserve the auto-regressive property
  • [1706. 03762] Attention Is All You Need - ar5iv
    Attention mechanisms have become an integral part of compelling sequence modeling and transduction models in various tasks, allowing modeling of dependencies without regard to their distance in the input or output sequences [2, 19] In all but a few cases [27], however, such attention mechanisms are used in conjunction with a recurrent network
  • Attention Is All You Need - arXiv. org
    Noam proposed scaled dot-product attention, multi-head attention and the parameter-free position representation and became the other person involved in nearly every detail Niki designed, implemented, tuned and evaluated countless model variants in our original codebase and tensor2tensor
  • TransMLA: Multi-Head Latent Attention Is All You Need
    View a PDF of the paper titled TransMLA: Multi-Head Latent Attention Is All You Need, by Fanxu Meng and 5 other authors
  • [2512. 19428] Attention Is Not What You Need - arXiv. org
    We revisit a basic question in sequence modeling: is explicit self-attention actually necessary for strong performance and reasoning? We argue that standard multi-head attention is best seen as a form of tensor lifting: hidden vectors are mapped into a high-dimensional space of pairwise interactions, and learning proceeds by constraining this lifted tensor through gradient descent This
  • arXiv. org e-Print archive
    This paper introduces the Transformer model, a novel architecture for natural language processing tasks based on self-attention mechanisms
  • [2501. 06425] Tensor Product Attention Is All You Need - arXiv. org
    View a PDF of the paper titled Tensor Product Attention Is All You Need, by Yifan Zhang and 6 other authors
  • Is Space-Time Attention All You Need for Video Understanding?
    We present a convolution-free approach to video classification built exclusively on self-attention over space and time Our method, named "TimeSformer," adapts the standard Transformer architecture to video by enabling spatiotemporal feature learning directly from a sequence of frame-level patches Our experimental study compares different self-attention schemes and suggests that "divided





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