Compact Representation

Compact representation research focuses on efficiently encoding complex data, such as images, language models, and 3D point clouds, into smaller, more manageable formats while preserving essential information. Current efforts utilize various techniques, including transformer-based architectures, implicit neural representations, and clustering methods like k-means, often incorporating low-rank approximations or other dimensionality reduction strategies to achieve this compression. These advancements are crucial for improving the efficiency and scalability of numerous applications, ranging from digital pathology and robotics to large language model evaluation and machine learning model training.