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AUTHORS: Chenghao Liu, Quang Pham, Doyen Sahoo, Donald Rose TL;DR: Nonstationary data, which changes its statistical properties over time, can make time series forecasting difficult. Despite the recent success of deep learning techniques for time series forecasting tasks, these methods are not scalable for applications where data arrives sequentially

TL;DR: The performance of existing time-series forecasting methods can degrade due to non-stationarity, where the statistical distribution of time-series data changes over time. Our new DeepTime method overcomes non-stationarity issues by leveraging a “forecasting as meta-learning” framework on deep time-index models. DeepTime achieves competitive accuracy on the long-sequence time-series

TL;DR: LAVIS (short for LAnguage-VISion) is an open-source deep learning library for language-vision research and applications, offering comprehensive support for a wide range of tasks, datasets, and state-of-the-art models. Featuring a unified interface and modular design, it’s easy to use off-the-shelf and to extend with new capabilities. With

Conference Overview This year marks the Tenth International Conference on Learning Representations ( ICLR [https://iclr.cc/Conferences/2022]), one of the premier academic conferences dedicated to advancing research in representation learning - a type of machine learning also referred to as feature learning or deep learning. ICLR features the latest