Arxiv Papers

Igor Melnyk
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. 6 GIỜ TRƯỚC

    [QA] Watermarking Autoregressive Image Generation

    https://arxiv.org/abs//2506.16349 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

    8 phút

  2. 6 GIỜ TRƯỚC

    Watermarking Autoregressive Image Generation

    https://arxiv.org/abs//2506.16349 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

    28 phút

  3. 6 GIỜ TRƯỚC

    [QA] Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights

    DnD introduces a prompt-conditioned parameter generator for LLMs, enabling rapid task-specific customization without separate training, achieving significant performance gains and lower overhead compared to traditional methods. https://arxiv.org/abs//2506.16406 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

    7 phút

  4. 6 GIỜ TRƯỚC

    Drag-and-Drop LLMs: Zero-Shot Prompt-to-Weights

    DnD introduces a prompt-conditioned parameter generator for LLMs, enabling rapid task-specific customization without separate training, achieving significant performance gains and lower overhead compared to traditional methods. https://arxiv.org/abs//2506.16406 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

    11 phút

  5. 1 NGÀY TRƯỚC

    [QA] Flat Channels to Infinity in Neural Loss Landscapes

    The paper characterizes special channels in neural network loss landscapes where slow loss decrease occurs, leading to gated linear units, enhancing understanding of gradient dynamics and optimization methods. https://arxiv.org/abs//2506.14951 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

    7 phút

  6. 1 NGÀY TRƯỚC

    Flat Channels to Infinity in Neural Loss Landscapes

    The paper characterizes special channels in neural network loss landscapes where slow loss decrease occurs, leading to gated linear units, enhancing understanding of gradient dynamics and optimization methods. https://arxiv.org/abs//2506.14951 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

    15 phút

  7. 1 NGÀY TRƯỚC

    [QA] Approximating Language Model Training Data from Weights

    The paper presents a method for approximating training data from model weights, improving performance significantly on classification tasks using a gradient-based approach to select relevant public documents. https://arxiv.org/abs//2506.15553 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

    8 phút

  8. 1 NGÀY TRƯỚC

    Approximating Language Model Training Data from Weights

    The paper presents a method for approximating training data from model weights, improving performance significantly on classification tasks using a gradient-based approach to select relevant public documents. https://arxiv.org/abs//2506.15553 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

    22 phút
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Giới Thiệu

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

Thông Tin

  • Nhà sáng tạo
    Igor Melnyk
  • Năm hoạt động
    2023 - 2025
  • Tập
    2 N
  • Xếp hạng
    Sạch
  • Bản quyền
    © Igor Melnyk
  • Trang web chương trình
    Arxiv Papers

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