Recurrent neural networks (RNNs) have significantly advanced the field of machine learning
(ML) by enabling the effective processing of sequential data. This paper provides a …

In recent years, machine learning (ML) and deep learning (DL) have shown remarkable
improvement in computer vision, natural language processing, stock prediction, forecasting …

Dropout is a powerful and widely used technique to regularize the training of deep neural
networks. Though effective and performing well, the randomness introduced by dropout …

Multi-horizon forecasting often contains a complex mix of inputs–including static (ie time-
invariant) covariates, known future inputs, and other exogenous time series that are only …

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 …

We present a new approach to modeling sequential data: the deep equilibrium model
(DEQ). Motivated by an observation that the hidden layers of many existing deep sequence …

When translating natural language questions into SQL queries to answer questions from a
database, contemporary semantic parsing models struggle to generalize to unseen …

Deep neural networks often work well when they are over-parameterized and trained with a
massive amount of noise and regularization, such as weight decay and dropout. Although …

Visual understanding goes well beyond object recognition. With one glance at an image, we
can effortlessly imagine the world beyond the pixels: for instance, we can infer people's …

Short-term traffic forecasting based on deep learning methods, especially recurrent neural
networks (RNN), has received much attention in recent years. However, the potential of RNN …