Summary of Gradient-free Training Of Recurrent Neural Networks Using Random Perturbations, by Jesus Garcia Fernandez et al.
Gradient-Free Training of Recurrent Neural Networks using Random Perturbations
by Jesus Garcia Fernandez, Sander Keemink, Marcel van Gerven
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 Recurrent neural networks (RNNs) have significant potential due to their sequential processing capabilities, but existing training methods face efficiency challenges. Backpropagation through time (BPTT), a widely used approach, has drawbacks such as interleaving forward and backward phases and storing exact gradient information. An alternative method is perturbation-based learning, which approximates gradients using random updates. This study presents a new approach to perturbation-based learning in RNNs that competes with BPTT while maintaining advantages over gradient-based learning. The proposed activity-based node perturbation (ANP) method operates in the time domain, leading to efficient learning and generalization. Experiments demonstrate similar performance, convergence time, and scalability compared to BPTT, outperforming standard node and weight perturbation methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recurrent neural networks are a type of artificial intelligence that can learn patterns over time. Right now, there’s a challenge in training these networks because the current method is slow and requires a lot of memory. This study looks at an alternative way to train RNNs that uses random updates instead of complicated calculations. The new approach is simple and doesn’t require storing a lot of information, which makes it faster and more efficient. When tested, this new method performed just as well as the current method but was much quicker. |
Keywords
» Artificial intelligence » Backpropagation » Generalization
