Variable Rate

Variable rate research focuses on dynamically adjusting parameters within systems to optimize performance across varying conditions, primarily addressing limitations of fixed-rate approaches. Current efforts concentrate on developing adaptable models, such as those employing differentiable quantizers or self-attention mechanisms, and refining learning algorithms like probabilistic learning rate schedulers to improve efficiency and control. This work is significant for enhancing bandwidth utilization in communications, improving the efficiency of image and video compression, and enabling more robust and adaptable machine learning models across diverse datasets and hardware constraints.