High Compression

High compression techniques aim to significantly reduce the size of data and models while preserving essential information, addressing the growing challenges of data storage and computational costs in various scientific domains. Current research focuses on developing novel compression algorithms, including those based on variational autoencoders, tensor networks, and quantization methods, often applied to large language models, neural networks for image and video processing, and climate datasets. These advancements are crucial for enabling wider accessibility to large datasets and models, improving the efficiency of machine learning applications, and facilitating scientific research in resource-constrained environments.