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

Multiscale simulation and homogenization of materials have become the major
computational technology as well as engineering tools in material modeling and material …

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 …

Adversarial training, which minimizes the maximal risk for label-preserving input
perturbations, has proved to be effective for improving the generalization of language …