Arxiv Papers

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

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. 3일 전

    [QA] Advancing Event Forecasting through Massive Training of Large Language Models: Challenges, Solutions, and Broader Impacts

    The paper advocates for research on training superforecaster-level event forecasting LLMs, addressing challenges in training methods and data acquisition to enhance predictive intelligence capabilities. https://arxiv.org/abs//2507.19477 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

    10분

  2. 3일 전

    Advancing Event Forecasting through Massive Training of Large Language Models: Challenges, Solutions, and Broader Impacts

    The paper advocates for research on training superforecaster-level event forecasting LLMs, addressing challenges in training methods and data acquisition to enhance predictive intelligence capabilities. https://arxiv.org/abs//2507.19477 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

    58분

  3. 4일 전

    [QA] AlphaGo Moment for Model Architecture Discovery

    ASI-ARCH is an autonomous AI system that innovates neural architecture discovery, surpassing human limitations and achieving state-of-the-art designs through extensive experimentation and scalable computational processes. https://arxiv.org/abs//2507.18074 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. 4일 전

    AlphaGo Moment for Model Architecture Discovery

    ASI-ARCH is an autonomous AI system that innovates neural architecture discovery, surpassing human limitations and achieving state-of-the-art designs through extensive experimentation and scalable computational processes. https://arxiv.org/abs//2507.18074 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

    24분

  5. 4일 전

    [QA] Learning without training: The implicit dynamics of in-context learning

    This paper explores how stacking a self-attention layer with an MLP in transformers enables Large Language Models to learn in context by implicitly modifying MLP weights based on presented examples. https://arxiv.org/abs//2507.16003 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

    9분

  6. 4일 전

    Learning without training: The implicit dynamics of in-context learning

    This paper explores how stacking a self-attention layer with an MLP in transformers enables Large Language Models to learn in context by implicitly modifying MLP weights based on presented examples. https://arxiv.org/abs//2507.16003 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

    13분

  7. 5일 전

    [QA] NABLA: Neighborhood Adaptive Block-Level Attention

    NABLA introduces a Neighborhood Adaptive Block-Level Attention mechanism for video diffusion transformers, enhancing efficiency and speed while maintaining quality, achieving up to 2.7 times faster training and inference. https://arxiv.org/abs//2507.13546 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

  8. 5일 전

    NABLA: Neighborhood Adaptive Block-Level Attention

    NABLA introduces a Neighborhood Adaptive Block-Level Attention mechanism for video diffusion transformers, enhancing efficiency and speed while maintaining quality, achieving up to 2.7 times faster training and inference. https://arxiv.org/abs//2507.13546 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

    13분
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평가 및 리뷰

5
최고 5점
3개의 평가

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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