Encoder Architecture

Encoder architectures are fundamental components of many machine learning models, tasked with efficiently transforming input data (e.g., text, images, audio) into meaningful representations for downstream tasks. Current research emphasizes improving encoder efficiency and representational power, focusing on architectures like Transformers, Conformers, and variations incorporating convolutional layers, attention mechanisms (e.g., sparse attention), and techniques like whitening to enhance feature quality. These advancements are driving improvements in diverse applications, including recommendation systems, speech recognition, video enhancement, and various other domains requiring effective feature extraction and representation learning.