Image-like data from quantum systems promises to offer greater insight into the physics of
correlated quantum matter. However, the traditional framework of condensed matter physics …

This research paper presents a novel class of restoration network architecture based on the
Volterra series formulation. By incorporating non-linearity into the system response function …

Temporal action proposal generation is a fundamental yet challenging to locate the temporal
action in untrimmed videos. Although current proposal generation methods can generate the …

We propose a deep structure encoder using Volterra Neural Networks (VNNs) to seek a
latent representation of multi-modal data whose features are jointly captured by a union of …

Convolutional Neural Networks (CNNs) have recently been introduced for addressing copy-
move forgery detection (CMFD). However, current CMFD CNN-based approaches have …

We propose a novel approach to image classification inspired by complex nonlinear
biological visual processing, whereby classical convolutional neural networks (CNNs) are …

We develop a compositional theory of nonlinear audio signal processing based on a
categorification of the Volterra series. We augment the classical definition of the Volterra …

Training of large-scale text-to-image and image-to-image models requires a huge amount of
annotated data. While text-to-image datasets are abundant, data available for instruction …

Transients in 1D/2D signals often carry critical information, and hence constitute useful
features for analysis and inference. A 2D signal, for example, is visually represented as an …

Seizures are episodes of abnormally excessive or synchronous electrical activity of localized
populations of neurons in the brain. Epilepsy is a condition in which these seizures are …