multi-modal-dl-frame.html

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title: "Multi-Modal Tabular Deep Learning with PyTorch Frame"
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      <h1>PyTorch Webinars</h1>
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        <img style="width:100%; max-width:600px; margin-bottom: 40px; display: block; margin-left: auto; margin-right: auto;" src="/assets/images/multi-modal-dl-frame.png" alt="AI-Powered Competitive Programming">            
        <h2>Multi-Modal Tabular Deep Learning with PyTorch Frame</h2>
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          <strong>Date</strong>: February 19, 12 pm PST
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          <strong>Speaker</strong>: Akihiro Nitta, Software Engineer, Kumo.ai
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          <a href="https://www.youtube.com/live/zPjLHf0X78w?feature=shared">Link to session video</a>
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          <a href="/assets/pytorch-frame-expert-exchange.pdf">Download slides</a>
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          In this talk, Akihiro introduced PyTorch Frame, a modular framework for multi-modal tabular deep learning. PyTorch Frame enables seamless integration with the PyTorch ecosystem, including PyTorch Geometric for graph-based message passing across relational data and Hugging Face Transformers for extracting rich text features. The talk also highlights its specialized data structures for efficiently handling sparse features, making PyTorch Frame an essential tool for modern tabular data.
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          Akihiro Nitta is a software engineer on the ML team at Kumo.ai and a core contributor to PyTorch Frame and PyTorch Geometric, with prior experience as a maintainer of PyTorch Lightning.
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