Large Deep Learning Model

Large deep learning models, characterized by their massive size and impressive performance across diverse tasks, are a central focus of current research. Efforts concentrate on improving training efficiency through techniques like model parallelism, quantization (including post-training and quantization-aware training), and novel optimization algorithms, while simultaneously addressing memory constraints and reducing computational costs. This research is crucial for making these powerful models more accessible and sustainable, impacting various fields from natural language processing and computer vision to remote sensing and scientific computing.