Byte Sized Breakthroughs

By: Arjun Srivastava
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  • Summary

  • Byte-Sized Breakthroughs offers concise audio summaries of recent AI research papers. Each episode breaks down a single paper in areas like machine learning, computer vision, or natural language processing, making it easier to stay current with AI advancements. The podcast covers topics such as large language models, mechanistic interpretability, and in-context learning. Episodes feature clear explanations of complex concepts, designed for efficient listening. Ideal for researchers, engineers, and AI enthusiasts with limited time, Byte-Sized Breakthroughs provides a starting point for exploring cutting-edge AI research. While offering overviews, listeners are encouraged to refer to original papers for comprehensive understanding. Curated by Arjun Srivastava, an engineer in the field, this podcast transforms spare moments into opportunities for learning about the latest in AI. Note: The voices you hear are not real people, but the content is carefully curated and reviewed.
    © 2024 Arjun Srivastava
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Episodes
  • TransAct Transformer-based Realtime User Action Model for Recommendation at Pinterest

    TransAct Transformer-based Realtime User Action Model for Recommendation at Pinterest
    Jul 8 2024
    Pinterest home feed reccomendation system. Needs to react to both long term interests + short term (even single session only) interests. Read full paper: https://arxiv.org/abs/2306.00248v1 Tags: Recommender Systems, Transformers, Systems and Performance
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    Less than 1 minute
  • Zero Bubble Pipeline Parallelism

    Zero Bubble Pipeline Parallelism
    Jul 8 2024
    Core idea is think about backward pass into two flows, one to compute grad wrt to parameters, and one to compute grad wrt to output of last layer, schedule so that you are always working instead of waiting (bubble). Read full paper: https://arxiv.org/abs/2401.10241 Tags: Systems and Performance, Deep Learning, Machine Learning
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    Less than 1 minute
  • The limits to learning a diffusion model

    The limits to learning a diffusion model
    Jul 8 2024
    Don't be confused by the title, diffusion here is not referring to diffusion as we use it today in context of image generation process, but more about modelling diffusive processes (like virus spread) This paper answers the question about 'how much data do we need, before we can figure out the final affected value' turns out this is a lot more thant people expect. Read full paper: https://arxiv.org/abs/2006.06373 Tags: Generative Models, Machine Learning, Deep Learning
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    Less than 1 minute
Natural History
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