Summary of Gradient-free Training Of Recurrent Neural Networks, by Erik Lien Bolager et al.
Gradient-free training of recurrent neural networks
by Erik Lien Bolager, Ana Cukarska, Iryna Burak, Zahra Monfared, Felix Dietrich
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Numerical Analysis (math.NA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to construct recurrent neural networks (RNNs) without relying on backpropagation through time. RNNs are widely used for tasks like time series analysis, forecasting, and modeling dynamical systems. However, training these networks is notoriously challenging due to exploding or vanishing gradients. The authors combine random feature networks and Koopman operator theory for dynamical systems to construct the weights and biases of a recurrent block without gradient-based methods. This approach alleviates common problems with backpropagation in RNNs. The paper showcases improved training times and forecasting accuracy on various datasets, including chaotic dynamical systems, control problems, and weather data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to train a special kind of computer program that can learn from patterns over time. This is called a recurrent neural network (RNN). But training these programs is really hard because they get stuck or don’t learn properly. In this paper, the authors find a new way to build RNNs without using a tricky math method called backpropagation. Instead, they combine two other methods: random feature networks and Koopman operator theory. This helps the RNNs learn faster and more accurately than usual. The authors tested their approach on different types of data, including weather forecasts and chaotic systems. The results show that this new way of building RNNs is better. |
Keywords
» Artificial intelligence » Backpropagation » Neural network » Rnn » Time series
