Deep learning, a branch of machine learning, is a frontier for artificial intelligence, aiming to
be closer to its primary goal—artificial intelligence. This paper mainly adopts the summary …

Along with the great success of deep neural networks, there is also growing concern about
their black-box nature. The interpretability issue affects people's trust on deep learning …

Camouflaged object detection (COD) aims to address the tough issue of identifying
camouflaged objects visually blended into the surrounding backgrounds. COD is a …

Infrared small target detection (IRSTD) refers to extracting small and dim targets from blurred
backgrounds, which has a wide range of applications such as traffic management and …

The idea of federated learning is to collaboratively train a neural network on a server. Each
user receives the current weights of the network and in turns sends parameter updates …

Cardiovascular disease (CVD) is a general term for a series of heart or blood vessels
abnormality that serves as a global leading reason for death. The earlier the abnormal heart …

Normalizing Flows are generative models which produce tractable distributions where both
sampling and density evaluation can be efficient and exact. The goal of this survey article is …

Because of their effectiveness in broad practical applications, LSTM networks have received
a wealth of coverage in scientific journals, technical blogs, and implementation guides …

We introduce a new family of deep neural network models. Instead of specifying a discrete
sequence of hidden layers, we parameterize the derivative of the hidden state using a …