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Learning Long-Time Dependencies with RNNs w/ Konstantin Rusch - #484

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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Stability Insights in RNNs

This chapter explores the stability of gradients in recurrent neural networks (RNNs), presenting two theorems that confirm gradient stability under certain conditions. It also examines advanced concepts like the Wen-Shing problem and the impact of coupled oscillators on gradient convergence across varying sequence lengths.

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