Cool, was thinking about this subject earlier today. Abstract: >This paper addresses the scalability challenge of architecture search by formulating the task

Cool, was thinking about this subject earlier today. Abstract: >This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques. Zoph's paper on architecture search: https://arxiv.org/abs/1611.01578 https://arxiv.org/abs/1611.01578 Concept of architecture search from ages ago: https://static1.squarespace.com/static/58e2a71bf7e0ab3ba886cea3/t/5909113c1b631b40f8137956/1493766462349/1989+neural+networks.pdf https://static1.squarespace.com/static/58e2a71bf7e0ab3ba886c...