Arxiv Papers

Igor Melnyk
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  • SCIENCE
  • UPDATED DAILY
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 Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

  1. 1 DAY AGO

    [QA] Solaris: A Foundation Model of the Sun

    Solaris, a foundation model for solar atmosphere forecasting, utilizes 13 years of solar imagery to outperform traditional models, showcasing its potential in solar physics. https://arxiv.org/abs//2411.16339 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 --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

    8 min

  2. 1 DAY AGO

    Solaris: A Foundation Model of the Sun

    Solaris, a foundation model for solar atmosphere forecasting, utilizes 13 years of solar imagery to outperform traditional models, showcasing its potential in solar physics. https://arxiv.org/abs//2411.16339 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 --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

    17 min

  3. 1 DAY AGO

    [QA] Even Sparser Graph Transformers

    Spexphormer improves Graph Transformers' efficiency by training a narrow network on an augmented graph, then using active connections to train a wider network, reducing memory requirements significantly. https://arxiv.org/abs//2411.16278 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 --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

    7 min

  4. 1 DAY AGO

    Even Sparser Graph Transformers

    Spexphormer improves Graph Transformers' efficiency by training a narrow network on an augmented graph, then using active connections to train a wider network, reducing memory requirements significantly. https://arxiv.org/abs//2411.16278 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 --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

    21 min

  5. 2 DAYS AGO

    [QA] Understanding LLM Embeddings for Regression

    https://arxiv.org/abs//2411.14708 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 --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

    7 min

  6. 2 DAYS AGO

    Understanding LLM Embeddings for Regression

    https://arxiv.org/abs//2411.14708 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 --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

    12 min

  7. 2 DAYS AGO

    [QA] Loss-to-Loss Prediction: Scaling Laws for All Datasets

    This paper develops a method to predict train loss across different datasets, revealing shifted power law relationships that improve accuracy over traditional single-dataset scaling laws. https://arxiv.org/abs//2411.12925 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 --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

    8 min

  8. 2 DAYS AGO

    Loss-to-Loss Prediction: Scaling Laws for All Datasets

    This paper develops a method to predict train loss across different datasets, revealing shifted power law relationships that improve accuracy over traditional single-dataset scaling laws. https://arxiv.org/abs//2411.12925 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 --- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

    22 min
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About

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 Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support

Information

  • Creator
    Igor Melnyk
  • Years Active
    2023 - 2024
  • Episodes
    1.7K
  • Rating
    Clean
  • Copyright
    © Igor Melnyk
  • Show Website
    Arxiv Papers

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