Generative Adversarial Networks (GANs) have shown remarkable success in producing
realistic-looking images in the computer vision area. Recently, GAN-based techniques are …

Dirichlet-based uncertainty (DBU) models are a recent and promising class of uncertainty-
aware models. DBU models predict the parameters of a Dirichlet distribution to provide fast …

Probabilistic time series forecasting has played critical role in decision-making processes
due to its capability to quantify uncertainties. Deep forecasting models, however, could be …

Over the last decade, various defense and security agencies have focused on methods of
data and information fusion across multiple domains (eg, space, air, land, sea, undersea …

Bayesian inference usually requires running potentially costly inference procedures
separately for every new observation. In contrast, the idea of amortized Bayesian inference …

Deep neural networks (DNNs) for time series classification have potential security concerns
due to their vulnerability to adversarial attacks. Previous work that perturbs time series …

We propose a post hoc saliency-based explanation framework for counterfactual reasoning
in probabilistic multivariate time-series forecasting (regression) settings. Building upon …

Abstract Generative Adversarial Networks (GANs) have proven their capability of generating
realistic-looking data and have been widely used in image related and time-series …

Real-world deep learning models developed for Time Series Forecasting are used in
several critical applications ranging from medical devices to the security domain. Many …

Multi-horizon time series forecasting, crucial across diverse domains, demands high
accuracy and speed. While AutoRegressive (AR) models excel in short-term predictions …