Multi-modal learning is widely adopted to learn the latent complementary information between different modalities in multi-modal medical image segmentation tasks. Nevertheless, the traditional ...

In seismic exploration, dense and evenly spatial sampled seismic traces are crucial for successful implementation of most seismic data processing and interpretation algorithms. Recently, numerous ...

CoAtNets combines convolutional and attention models to enhance performance in deep learning tasks. This hybrid model has demonstrated state-of-the-art results in image classification, particularly on ...

Abstract: In industrial processes, data-driven soft sensors have played an important role for the effective process control, optimization, and monitoring. Deep learning technique has been widely used ...

Using logical clauses to represent patterns, Tsetlin machines (https://arxiv.org/abs/1804.01508) have obtained competitive performance in terms of accuracy, memory ...

Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many ...

Abstract: Problem exists to effectively learn technical topics requiring sound mathematical practices. Especially in engineering domain, detailed mathematical illustrations sometimes robs the time of ...