Ruohan Gao and Kristen Grauman
International Conference on Machine Learning (ICML) 2019 has got 3424 submissions and 744 accepted (accpetance rate is 22.6%). The result can be found here
Semi-supervised learning (SSL) seeks to largely alleviate the need for labeled data by allowing a model to leverage unlabeled data. Many recent approaches for semi-supervised learning add a loss term which is computed on unlabeled data and encourages the model to generalize better to unseen data.
Deep Learning has enabled remarkable progress over the last years on a variety of tasks, such as image recognition, speech recognition, and machine translation. One crucial aspect for this progress are novel neural architectures. Currently employed architectures have mostly been developed manually by human experts, which is a time-consuming and error-prone process. Because of this, there is growing interest in automated neural architecture search methods. We provide an overview of existing work in this field of research and categorize them according to three dimensions: search space, search strategy, and performance estimation strategy.
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