• Keeping Neural Networks Simple

  • 2024/11/02
  • 再生時間： 7 分
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Keeping Neural Networks Simple

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  • This episode breaks down 'Keeping Neural Networks Simple' paper, which explores methods for improving the generalisation of neural networks, particularly in scenarios with limited training data. The authors argue for the importance of minimising the information content of the network weights, drawing upon the Minimum Description Length (MDL) principle. They propose using noisy weights, which can be communicated more efficiently, and develop a framework for calculating their impact on the network's performance. The paper introduces an adaptive mixture of Gaussians prior for coding weights, enabling greater flexibility in capturing weight distribution patterns. Preliminary results demonstrate the potential of this approach, particularly when compared to standard weight-decay methods.

    Audio : (Spotify) https://open.spotify.com/episode/6R86n2gXJkO412hAlig8nS?si=Hry3Y2PiQUOs2MLgJTJoZg

    Paper: https://www.cs.toronto.edu/~hinton/absps/colt93.pdf
    続きを読む 一部表示

あらすじ・解説

This episode breaks down 'Keeping Neural Networks Simple' paper, which explores methods for improving the generalisation of neural networks, particularly in scenarios with limited training data. The authors argue for the importance of minimising the information content of the network weights, drawing upon the Minimum Description Length (MDL) principle. They propose using noisy weights, which can be communicated more efficiently, and develop a framework for calculating their impact on the network's performance. The paper introduces an adaptive mixture of Gaussians prior for coding weights, enabling greater flexibility in capturing weight distribution patterns. Preliminary results demonstrate the potential of this approach, particularly when compared to standard weight-decay methods.

Audio : (Spotify) https://open.spotify.com/episode/6R86n2gXJkO412hAlig8nS?si=Hry3Y2PiQUOs2MLgJTJoZg

Paper: https://www.cs.toronto.edu/~hinton/absps/colt93.pdf
続きを読む 一部表示

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