Diffusion models have emerged as a powerful new family of deep generative models with
record-breaking performance in many applications, including image synthesis, video …

Generative adversarial network (GAN) studies have grown exponentially in the past few
years. Their impact has been seen mainly in the computer vision field with realistic image …

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …

The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …

Deep learning performs remarkably well on many time series analysis tasks recently. The
superior performance of deep neural networks relies heavily on a large number of training …

The building sector accounts for 36% of the total global energy usage and 40% of
associated Carbon Dioxide emissions. Therefore, the forecasting of building energy …

In this work, we propose TimeGrad, an autoregressive model for multivariate probabilistic
time series forecasting which samples from the data distribution at each time step by …

This explainer document aims to provide an overview of the current state of the rapidly
expanding work on synthetic data technologies, with a particular focus on privacy. The …

This paper presents a critical approach to the non-intrusive load monitoring (NILM) problem,
by thoroughly reviewing the experimental framework of both legacy and state-of-the-art …

Time series forecasting has been a widely explored task of great importance in many
applications. However, it is common that real-world time series data are recorded in a short …