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  • title: On Scrambling Phenomena for Randomly Initialized Recurrent Networks
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            On Scrambling Phenomena for Randomly Initialized Recurrent Networks
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            On Scrambling Phenomena for Randomly Initialized Recurrent Networks

            Nov 28, 2022

            Speakers

            VC

            Vaggos Chatziafratis

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            Ioannis Panageas

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            CS

            Clayton Sanford

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            About

            Recurrent Neural Networks (RNNs) frequently exhibit complicated dynamics, and their sensitivity to the initialization process often renders them notoriously hard to train. Recent works have shed light on such phenomena analyzing when exploding or vanishing gradients may occur, either of which is detrimental for training dynamics. In this paper, we point to a formal connection between RNNs and chaotic dynamical systems and prove a qualitatively stronger phenomenon about RNNs than what exploding g…

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