Forecasting has always been at the forefront of decision making and planning. The
uncertainty that surrounds the future is both exciting and challenging, with individuals and …

Variational autoencoders (VAEs) are powerful deep generative models widely used to
represent high-dimensional complex data through a low-dimensional latent space learned …

Online misinformation promotes distrust in science, undermines public health, and may drive
civil unrest. During the coronavirus disease 2019 pandemic, Facebook—the world's largest …

State estimation of dynamical systems in real-time is a fundamental task in signal
processing. For systems that are well-represented by a fully known linear Gaussian state …

Time series forecasting is an important problem across many domains, including predictions
of solar plant energy output, electricity consumption, and traffic jam situation. In this paper …

Background Alcohol use is a leading risk factor for global disease burden, and data on
alcohol exposure are crucial to evaluate progress in achieving global non-communicable …

Noninvasive behavioral tracking of animals during experiments is critical to many scientific
pursuits. Extracting the poses of animals without using markers is often essential to …

We present a novel approach to probabilistic time series forecasting that combines state
space models with deep learning. By parametrizing a per-time-series linear state space …

In this work, we examine a novel forecasting approach for COVID-19 case prediction that
uses Graph Neural Networks and mobility data. In contrast to existing time series forecasting …

Probabilistic forecasting, ie, estimating a time series' future probability distribution given its
past, is a key enabler for optimizing business processes. In retail businesses, for example …