Optimal transport (OT) theory can be informally described using the words of the French
mathematician Gaspard Monge (1746–1818): A worker with a shovel in hand has to move a …

Mathematical optimization is now widely regarded as an indispensable modeling and
solution tool for the design of wireless communications systems. While optimization has …

Automatic differentiation (autodiff) has revolutionized machine learning. Itallows to express
complex computations by composing elementary ones in creativeways and removes the …

Fusing hyperspectral images (HSIs) with multispectral images (MSIs) of higher spatial
resolution has become an effective way to sharpen HSIs. Recently, deep convolutional …

A graph is usually formed to reveal the relationship between data points and graph structure
is encoded by the affinity matrix. Most graph-based multiview clustering methods use …

We introduce Parseval networks, a form of deep neural networks in which the Lipschitz
constant of linear, convolutional and aggregation layers is constrained to be smaller than $1 …

Training neural networks under a strict Lipschitz constraint is useful for provable adversarial
robustness, generalization bounds, interpretable gradients, and Wasserstein distance …

We propose sparsemax, a new activation function similar to the traditional softmax, but able
to output sparse probabilities. After deriving its properties, we show how its Jacobian can be …

Sequence-to-sequence models are a powerful workhorse of NLP. Most variants employ a
softmax transformation in both their attention mechanism and output layer, leading to dense …

Hyperspectral cameras capture images with many narrow spectral channels, which densely
sample the electromagnetic spectrum. The detailed spectral resolution is useful for many …