Konstantin Rusch

PhD student at ETH Zurich, focusing on machine learning and its applications in scientific computing. His research explores novel RNN architectures for learning long-time dependencies.

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May 17, 2021 • 38min

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

In this discussion, Konstantin Rusch, a PhD student at ETH Zurich, dives into innovative recurrent neural networks (RNNs) aimed at tackling long-time dependencies. He shares insights from his papers on coRNN and uniCORNN, inspired by neuroscience, and how these architectures compare to traditional models like LSTMs. Konstantin also reveals challenges in ensuring gradient stability and innovative techniques that enhance RNNs' expressive power. Plus, he discusses his ambitions for future advancements in memory efficiency and performance.

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