Limited Number

Research on "limited number" scenarios focuses on developing methods to achieve high performance with constrained resources, whether it's limited data, computational power, or communication bandwidth. Current efforts involve adapting existing models like Vision Transformers and reinforcement learning algorithms, often incorporating techniques like sparsity, factorization, and active learning to improve efficiency and robustness. These advancements are crucial for deploying machine learning models in resource-constrained environments and for addressing challenges in data scarcity, making them relevant across diverse fields from video generation to speech recognition and financial modeling.