Arxiv Papers

Igor Melnyk
  • 5.0 (3)
  • CIENCIA
  • CADA DÍA
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. HACE 5 H

    [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 min

  2. HACE 5 H

    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 min

  3. HACE 5 H

    [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 min

  4. HACE 5 H

    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 min

  5. HACE 2 DÍAS

    [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

    8 min

  6. HACE 2 DÍAS

    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

    17 min

  7. HACE 2 DÍAS

    [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

    9 min

  8. HACE 2 DÍAS

    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

    12 min
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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

Información

  • Creador
    Igor Melnyk
  • Años de actividad
    2023 - 2025
  • Episodios
    2 k
  • Clasificación
    Apto
  • Copyright
    © Igor Melnyk
  • Mostrar sitio web
    Arxiv Papers

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