Arxiv Papers

Igor Melnyk
  • 5.0(3개의 평가)
  • 과학
  • 매일 업데이트
Arxiv Papers

Running out of time to catch up with new arXiv papers? We take the most impactful papers and present them as convenient podcasts. If you're a visual learner, we offer these papers in an engaging video format. Our service fills the gap between overly brief paper summaries and time-consuming full paper reads. You gain academic insights in a time-efficient, digestible format. Code behind this work: https://github.com/imelnyk/ArxivPapers

  1. 21시간 전

    [QA] Sampling from Your Language Model One Byte at a Time

    This paper presents a method to convert autoregressive language models with BPE tokenizers into character-level models, addressing tokenization issues and enabling model interoperability and improved performance through ensemble and proxy-tuning. https://arxiv.org/abs//2506.14123 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    7분

  2. 21시간 전

    Sampling from Your Language Model One Byte at a Time

    This paper presents a method to convert autoregressive language models with BPE tokenizers into character-level models, addressing tokenization issues and enabling model interoperability and improved performance through ensemble and proxy-tuning. https://arxiv.org/abs//2506.14123 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    14분

  3. 21시간 전

    [QA] Don't throw the baby out with the bathwater: How and why deep learning for ARC

    This paper demonstrates that deep learning, through on-the-fly training and innovative techniques, significantly enhances performance on the Abstraction and Reasoning Corpus, achieving state-of-the-art results. https://arxiv.org/abs//2506.14276 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    8분

  4. 21시간 전

    Don't throw the baby out with the bathwater: How and why deep learning for ARC

    This paper demonstrates that deep learning, through on-the-fly training and innovative techniques, significantly enhances performance on the Abstraction and Reasoning Corpus, achieving state-of-the-art results. https://arxiv.org/abs//2506.14276 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    33분

  5. 1일 전

    [QA] What Happens During the Loss Plateau? Understanding Abrupt Learning in Transformers

    This study explores abrupt learning in shallow Transformers, revealing a performance plateau characterized by repetition bias and representation collapse, with attention map learning as a critical bottleneck. https://arxiv.org/abs//2506.13688 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    7분

  6. 1일 전

    What Happens During the Loss Plateau? Understanding Abrupt Learning in Transformers

    This study explores abrupt learning in shallow Transformers, revealing a performance plateau characterized by repetition bias and representation collapse, with attention map learning as a critical bottleneck. https://arxiv.org/abs//2506.13688 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    20분

  7. 1일 전

    [QA] MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

    https://arxiv.org/abs//2506.13585 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    8분

  8. 1일 전

    MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention

    https://arxiv.org/abs//2506.13585 YouTube: https://www.youtube.com/@ArxivPapers TikTok: https://www.tiktok.com/@arxiv_papers Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016 Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers

    25분
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소개

Running out of time to catch up with new arXiv papers? We take the most impactful papers and present them as convenient podcasts. If you're a visual learner, we offer these papers in an engaging video format. Our service fills the gap between overly brief paper summaries and time-consuming full paper reads. You gain academic insights in a time-efficient, digestible format. Code behind this work: https://github.com/imelnyk/ArxivPapers

정보

  • 제작진
    Igor Melnyk
  • 방송 연도
    2023년 - 2025년
  • 에피소드
    2천
  • 등급
    전체 연령 사용가
  • 저작권
    © Igor Melnyk
  • 웹사이트 보기
    Arxiv Papers

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