Chronic kidney disease (CKD) is a condition characterized by progressive loss of kidney
function over time. It describes a clinical entity that causes kidney damage and affects the …

Deep neural networks (DNNs) generalize remarkably well without explicit regularization
even in the strongly over-parametrized regime where classical learning theory would …

This article introduces a special issue on the interaction between the rapidly expanding field
of machine learning and ongoing research in physics. The first half of the papers in this …

Despite tremendous success of the stochastic gradient descent (SGD) algorithm in deep
learning, little is known about how SGD finds generalizable solutions at flat minima of the …

Modern deep networks are trained with stochastic gradient descent (SGD) whose key
hyperparameters are the number of data considered at each step or batch size B, and the …

Existing algorithms for aligning cross-lingual word vector spaces assume that vector spaces
are approximately isomorphic. As a result, they perform poorly or fail completely on non …

The study of energy landscapes as a conceptual framework, and a source of novel
computational tools, is an active area of research in chemistry and physics. The energy …

The higher heating value (HHV) provides information about the quantity of energy contained
in a fuel such as biomass. Correlations and models can be developed to predict biomass …

Generalization is one of the most important problems in deep learning, where there exist
many low-loss solutions due to overparametrization. Previous empirical studies showed a …

Datasets such as images, text, or movies are embedded in high-dimensional spaces.
However, in important cases such as images of objects, the statistical structure in the data …