N-BEATS: Neural Basis Expansion Analysis For Interpretable Time Series Forecasting

By Boris N. Oreshkin, Dmitri Carpov, Nicolas Chapados (Element AI), and Yoshua Bengio (MILA), 2019

This paper presents a block-based deep neural architecture for univariate time series point forecasting that is similar to very deep models (e.g. ResNet) used in more common deep learning applications such as image recognition. Furthermore, the authors demonstrate how their approach can be used to build predictive models that are interpretable.

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Compounding the Performance Improvements of Assembled Techniques in a Convolutional Neural Network

By Jungkyu Lee, Taeryun Won, and Kiho Hong, Clova Vision, NAVER Corp, 2019

A great review of many state-of-the-art tricks that can be used to improve the performance of a deep convolutional network (ResNet), combined with actual implementation details, source code, and performance results. A must read for all Kaggle competitors or anyone who wants to achieve maximum performance on computer vision tasks.

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