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<span class="release-stage" >pre-print</span>
Convolution is one of the most essential components of architectures used in computer vision. As machine learning moves towards reducing the expert bias and learning it from data, a natural next step seems to be learning convolution-like structures from scratch. This, however, has proven elusive. For example, current state-of-the-art architecture search algorithms use convolution as one of the existing modules rather than learning it from data. In an attempt to understand the inductive bias<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.13657v1">arXiv:2007.13657v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/kkkqf3vigrfxhabsagr7oxe5wq">fatcat:kkkqf3vigrfxhabsagr7oxe5wq</a> </span>
more »... gives rise to convolutions, we investigate minimum description length as a guiding principle and show that in some settings, it can indeed be indicative of the performance of architectures. To find architectures with small description length, we propose β-LASSO, a simple variant of LASSO algorithm that, when applied on fully-connected networks for image classification tasks, learns architectures with local connections and achieves state-of-the-art accuracies for training fully-connected nets on CIFAR-10 (85.19 bridging the gap between fully-connected and convolutional nets.
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