Arxiv Papers

Igor Melnyk
  • 5,0 (3)
  • CIÊNCIA
  • DIÁRIO
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. HÁ 1 DIA

    [QA] GenRecal: Generation after Recalibration from Large to Small Vision-Language Models

    GenRecal is a novel distillation framework for vision-language models that enhances knowledge transfer across diverse architectures, improving performance on resource-constrained devices while outperforming large-scale VLMs. https://arxiv.org/abs//2506.15681 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

    8min

  2. HÁ 1 DIA

    GenRecal: Generation after Recalibration from Large to Small Vision-Language Models

    GenRecal is a novel distillation framework for vision-language models that enhances knowledge transfer across diverse architectures, improving performance on resource-constrained devices while outperforming large-scale VLMs. https://arxiv.org/abs//2506.15681 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

    17min

  3. HÁ 1 DIA

    [QA] ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning in LLMs

    https://arxiv.org/abs//2506.15211 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

    9min

  4. HÁ 1 DIA

    ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning in LLMs

    https://arxiv.org/abs//2506.15211 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

    12min

  5. HÁ 2 DIAS

    [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

    7min

  6. HÁ 2 DIAS

    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

    14min

  7. HÁ 2 DIAS

    [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

    8min

  8. HÁ 2 DIAS

    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

    33min
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Classificações e avaliações

5
de 5
3 avaliações

Sobre

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

Informações

  • Criado por
    Igor Melnyk
  • Anos de atividade
    2023 - 2025
  • Episódios
    2 mil
  • Classificação
    Livre
  • Copyright
    © Igor Melnyk
  • Site do podcast
    Arxiv Papers

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