Attention mechanism is the key to large language models, and attention matrix serves as an
algorithmic and computational bottleneck for such a scheme. In this paper, we define two …

Scene classification is one of the fundamental techniques shared by many basic remote
sensing tasks with a wide range of applications. As the demands of catering with situations …

One notable weakness of current machine learning algorithms is the poor ability of models
to solve new problems without forgetting previously acquired knowledge. The Continual …

Humans have the ability to acquire, retain and transfer knowledge over their lifespan. For
intelligent agents to achieve fluent longitudinal interaction, they need to continually retain …

Using task-specific components within a neural network in continual learning (CL) is a
compelling strategy to address the stability-plasticity dilemma in fixed-capacity models …

One weakness of machine learning algorithms is the poor ability of models to solve new
problems without forgetting previously acquired knowledge. The Continual Learning (CL) …

The continuous emergence of new targets in open scenarios leads to a substantial decrease
in the performance of Inverse Synthetic Aperture Radar (ISAR) recognition systems. Also …

Progressive learning is a deep learning framework in which tasks are learned sequentially,
with the capacity to leverage past knowledge from previously acquired tasks to aid in the …

We propose an online continual learning algorithm based on an enhanced Random Vector
Functional Link Network (OCL-eRVFL), that learns a sequence of tasks continually, where …

Overfitting is a phenomenon that occurs when a machine learning model is trained for too
long and focused too much on the exact fitness of the training samples to the provided …