Sparse Softmax

Sparse softmax methods aim to improve the efficiency and performance of softmax functions, commonly used in multi-class classification, by reducing computational complexity and addressing limitations in high-dimensional settings. Current research focuses on developing algorithms that incorporate sparsity, such as through the use of sparsemax and related techniques within larger architectures like differentiable architecture search (DARTS) and feature selection methods (e.g., using learnable sparse masks). These advancements offer potential benefits in various applications, including faster training of large models and improved performance in resource-constrained environments.