Encoding Distribution

Encoding distribution research focuses on efficiently and effectively representing data in a compressed format suitable for machine learning tasks. Current efforts concentrate on developing adaptable encoding methods, such as those leveraging neural networks (including CNNs and transformers) and Fourier transforms, to handle diverse data types (e.g., geospatial data, language, point clouds) and improve compression rates. This work is significant for advancing data democratization in fields like healthcare, enhancing the performance of deep learning models, and providing more accurate and efficient representations of complex data for various applications, including brain-computer interfaces and spatial reasoning.