Disentangled Representation Learning (DRL) aims to learn a model capable of identifying
and disentangling the underlying factors hidden in the observable data in representation …

In this paper, we review recent work in media forensics for digital images, video, audio
(specifically speech), and documents. For each data modality, we discuss synthesis and …

The carcinogenicity of drugs can have a serious impact on human health, so carcinogenicity
testing of new compounds is very necessary before being put on the market. Currently, many …

Numerous advancements in various fields, including pattern recognition and image
classification, have been made thanks to modern computer vision and machine learning …

When designing Convolutional Neural Networks (CNNs), one must select the size\break of
the convolutional kernels before training. Recent works show CNNs benefit from different …

Spatial transcriptomics unveils the complex dynamics of cell regulation and transcriptomes,
but it is typically cost-prohibitive. Predicting spatial gene expression from histological images …

The profound impact of deep learning and particularly of convolutional neural networks
(CNNs) in automatic image processing has been decisive for the progress and evolution of …

Flotation is an important separation method for coal preparation, where ash content is critical
to coal product quality. However, the absence of fast and accurate ash determination of coal …

To additional understand the wind speed prediction of every wind energy facility in several
geographical locations and environments within the Wind Power Centralized Control Center …

Deep neural networks achieve outstanding results in a large variety of tasks, often
outperforming human experts. However, a known limitation of current neural architectures is …