Summary of Training Deep Neural Classifiers with Soft Diamond Regularizers, by Olaoluwa Adigun and Bart Kosko
Training Deep Neural Classifiers with Soft Diamond Regularizers
by Olaoluwa Adigun, Bart Kosko
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 We introduce the soft diamond regularizer, a novel approach that improves synaptic sparsity and maintains classification accuracy in deep neural networks. This parametrized regularizer outperforms state-of-the-art methods like hard-diamond Laplacian regularizers used in Lasso regression and classification. The new regularizer uses thick-tailed symmetric alpha-stable bell-curve synaptic weight priors, which are not Gaussian and have thicker tails than traditional Gaussian distributions. To train with these priors, a precomputed look-up table was developed to remove the computational bottleneck. We tested the soft diamond regularizers on three datasets: CIFAR-10, CIFAR-100, and Caltech-256. The results show improved accuracy for the classifiers, with improvements of 4.57%, 4.27%, and 6.69% respectively. The soft diamond regularizers also outperformed L2 regularizers in all test cases. Additionally, they demonstrated better sparsity and classification accuracy compared to L1 lasso or Laplace regularizers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make neural networks more accurate and efficient. It’s called the soft diamond regularizer. This method helps deep learning models learn more effectively by adding some extra rules to keep the weights of the model sparse (meaning fewer connections). The results show that this approach works better than other methods for three different datasets: images of animals, vehicles, and objects. The improvements are significant, with accuracy increasing by 4-6% in each case. Overall, this new method is useful for improving neural network performance. |
Keywords
* Artificial intelligence * Classification * Deep learning * Neural network * Regression
