Summary of Adaptive Random Fourier Features Training Stabilized by Resampling with Applications in Image Regression, By Aku Kammonen et al.
Adaptive Random Fourier Features Training Stabilized By Resampling With Applications in Image Regression
by Aku Kammonen, Anamika Pandey, Erik von Schwerin, Raúl Tempone
First submitted to arxiv on: 8 Oct 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 This paper introduces an enhanced adaptive random Fourier features (ARFF) training algorithm for shallow neural networks, building upon a previous work that combined ARFF with Metropolis sampling. The new method uses a particle filter-type resampling technique to stabilize the training process and reduce sensitivity to parameter choices. This allows for the omission of the Metropolis test, reducing hyperparameters by one and computational cost per iteration compared to the original ARFF method. Numerical experiments demonstrate the effectiveness of the proposed algorithm in function regression tasks as a standalone method and as a pretraining step before gradient-based optimization using Adam optimizer. Additionally, the paper applies the algorithm to a simple image regression problem, showcasing its utility in sampling frequencies for the random Fourier features (RFF) layer of coordinate-based multilayer perceptrons. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper improves an old way of training neural networks called adaptive random Fourier features (ARFF). They added a new trick that makes it more stable and easier to use. This means they don’t need to do as much extra work or make as many decisions about how to train the network. They tested this new method on some math problems and showed it works well. They also used it to solve an image recognition problem, which is a real-world application. |
Keywords
» Artificial intelligence » Optimization » Pretraining » Regression
