With the success of language pretraining, it is highly desirable to develop more efficient architectures of good scalability that can exploit the abundant unlabeled data at a lower cost.
Ranked #6 on Reading Comprehension on RACE
Keywords: Multivariate Time Series, Change-point Detection, Graph Neural Networks
We consider the setting of semi-supervised cross-lingual understanding, where labeled data is available in a source language (English), but only unlabeled data is available in the target language.
With latent variables, stochastic recurrent models have achieved state-of-the-art performance in modeling sound-wave sequence.
In this paper, we combine the ideas from both stochastic latent variables and dilated convolutions, and propose a new architecture to model sequential data, termed as Stochastic WaveNet, where stochastic latent variables are injected into the WaveNet structure.
Convolution Neural Network (CNN) has gained tremendous success in computer vision tasks with its outstanding ability to capture the local latent features.
Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation.
Ranked #5 on Univariate Time Series Forecasting on Solar-Power