Learning Long-Time Dependencies with RNNs w/ Konstantin Rusch - #484
Published:May 17, 2021 16:28
•1 min read
•Practical AI
Analysis
This article summarizes a podcast episode from Practical AI featuring Konstantin Rusch, a PhD student at ETH Zurich. The episode focuses on Rusch's research on recurrent neural networks (RNNs) and their ability to learn long-time dependencies. The discussion centers around his papers, coRNN and uniCORNN, exploring the architecture's inspiration from neuroscience, its performance compared to established models like LSTMs, and his future research directions. The article provides a brief overview of the episode's content, highlighting key aspects of the research and the conversation.
Key Takeaways
- •The episode discusses coRNN and uniCORNN, novel RNN architectures.
- •The research draws inspiration from neuroscience.
- •The episode compares the performance of the new architectures to existing models like LSTMs.
- •The episode covers the future research goals of Konstantin Rusch.
Reference
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