Image classification has always been a hot research direction in the world, and the
emergence of deep learning has promoted the development of this field. Convolutional …

With the broader and highly successful usage of machine learning (ML) in industry and the
sciences, there has been a growing demand for explainable artificial intelligence (XAI) …

Large language models (LLMs) with hundreds of billions of parameters have sparked a new
wave of exciting AI applications. However, they are computationally expensive at inference …

Over the past few years, neural networks have exhibited remarkable results for various
applications in machine learning and computer vision. Weight initialization is a significant …

The learning rate warmup heuristic achieves remarkable success in stabilizing training,
accelerating convergence and improving generalization for adaptive stochastic optimization …

Plasticity, the ability of a neural network to quickly change its predictions in response to new
information, is essential for the adaptability and robustness of deep reinforcement learning …

For a machine learning model to generalize well, one needs to ensure that its decisions are
supported by meaningful patterns in the input data. A prerequisite is however for the model …

Abstract Explainable Artificial Intelligence (xAI) is an established field with a vibrant
community that has developed a variety of very successful approaches to explain and …

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that
aims to obtain a high-resolution output from one of its low-resolution versions. Recently …

Abstract Batch Normalization (BatchNorm) is a widely adopted technique that enables faster
and more stable training of deep neural networks (DNNs). Despite its pervasiveness, the …