Gradient Vector

Gradient vectors, representing the direction of steepest ascent of a function, are central to many optimization problems in machine learning and other fields. Current research focuses on improving gradient-based optimization algorithms, including techniques like adaptive gradient regularization and normalization to enhance training stability and efficiency, as well as developing methods for efficient gradient compression and retrieval for large-scale models. These advancements are crucial for improving the performance and scalability of machine learning models across diverse applications, such as image processing, natural language processing, and 3D object detection, while also addressing challenges like Byzantine failures in distributed learning.