Summary of Convolutional Differentiable Logic Gate Networks, by Felix Petersen et al.
Convolutional Differentiable Logic Gate Networks
by Felix Petersen, Hilde Kuehne, Christian Borgelt, Julian Welzel, Stefano Ermon
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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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 A novel approach for learning logic gate networks via differentiable relaxation is extended to scale up their inference capabilities by incorporating deep logic gate tree convolutions, logical OR pooling, and residual initializations. This leads to a significant increase in the order of magnitude, allowing logic gate networks to be applied to larger datasets. The proposed method achieves state-of-the-art accuracy on CIFAR-10 using only 61 million logic gates, which is 29 times smaller than the previous best model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Logic gate networks are a new way to do machine learning that’s faster and more efficient than regular neural networks. They work by using simple building blocks called logic gates, like NAND or OR, which can be executed quickly on computers. A team of researchers took this idea and made it better by adding some extra tricks, like tree convolutions and pooling. This helps them process bigger datasets and get even better results. On a test with pictures, they got an accuracy of 86.29% using only 61 million logic gates, which is the best anyone has done so far. |
Keywords
* Artificial intelligence * Inference * Machine learning
