Uncertainty quantification (UQ) methods play a pivotal role in reducing the impact of
uncertainties during both optimization and decision making processes. They have been …

Deep learning (DL) has emerged as a powerful subset of machine learning (ML) and
artificial intelligence (AI), outperforming traditional ML methods, especially in handling …

An Introduction to Variational Autoencoders Page 1 An Introduction to Variational Autoencoders
Page 2 Other titles in Foundations and Trends R in Machine Learning Computational Optimal …

Current approaches for multi-horizon time series forecasting using recurrent neural networks
(RNNs) focus on issuing point estimates, which is insufficient for decision-making in critical …

Meta-learning for few-shot learning entails acquiring a prior over previous tasks and
experiences, such that new tasks be learned from small amounts of data. However, a critical …

Strength exercise is a strategy applied in sports and physical training processes. It may
induce skeletal muscle hypertrophy. The hypertrophy is dependent on the eccentric muscle …

Although reinforcement learning methods can achieve impressive results in simulation, the
real world presents two major challenges: generating samples is exceedingly expensive …

Reliable uncertainty estimation for time series prediction is critical in many fields, including
physics, biology, and manufacturing. At Uber, probabilistic time series forecasting is used for …

Artificial Neural Networks are connectionist systems that perform a given task by learning on
examples without having prior knowledge about the task. This is done by finding an optimal …

Several recent papers investigate Active Learning (AL) for mitigating the data dependence
of deep learning for natural language processing. However, the applicability of AL to real …