Non Embedding

Non-embedding components in deep learning models are a growing area of research focusing on optimizing their efficiency and effectiveness. Current efforts concentrate on reducing the size and computational cost of these components, such as feed-forward networks and attention mechanisms, within various architectures including Transformers and those used for recommendation systems and scientific machine learning (solving PDEs). This research aims to improve model performance while simultaneously decreasing resource requirements, leading to faster training and inference times and broader applicability across diverse domains.