Google-Scholar-API

{
  "found": true,
  "data": {
    "query": "attention is all you need",
    "results": [
      {
        "title": "Attention is all you need",
        "link": "https://proceedings.neurips.cc/paper/2017/attention-is-all-you-need",
        "snippet": "to attend to all positions in the decoder up to and including that position. We need to prevent leftward information flow. We implement this inside of scaled dot product attention by masking out values.",
        "publicationInfo": "A Reyes, N Okafor, M Lindqvist - Advances in Neural Information Processing Systems, 2017",
        "year": 2017,
        "citedBy": 272137,
        "pdfUrl": "https://proceedings.neurips.cc/paper/2017/file/attention-is-all-you-need.pdf",
        "id": "cites=9091159518698614860"
      },
      {
        "title": "Is attention all you need?",
        "link": "https://link.springer.com/chapter/10.1007/978-3-031-attention-review",
        "snippet": "We show how transformers and attention have opened up new dimensions of inquiry in cognitive science, who first introduced intra attention, what we now call self attention.",
        "publicationInfo": "P Moreau - From Human Attention to Computational Attention, 2025 - Springer",
        "year": 2025,
        "citedBy": 15,
        "id": "cites=4471820556309981234"
      },
      {
        "title": "Attention is all you need in speech separation",
        "link": "https://ieeexplore.ieee.org/document/attention-speech-separation",
        "snippet": "Transformers are emerging as a natural alternative to standard RNNs, replacing recurrent computations with a multi head attention mechanism. In this paper, we propose the SepFormer.",
        "publicationInfo": "C Subasi, M Ranieri, S Cornejo - ICASSP 2021, 2021 - ieeexplore.ieee.org",
        "year": 2021,
        "citedBy": 1135,
        "pdfUrl": "https://arxiv.org/pdf/2010.13154.pdf",
        "id": "cites=6602391847710235589"
      }
    ]
  }
}