Token Selection

Token selection in neural networks aims to optimize computational efficiency and model performance by strategically choosing a subset of input tokens for processing. Current research focuses on developing efficient algorithms for selecting these tokens, often within transformer architectures, using methods like reward function estimation, probability-based pruning, and core-set selection. These advancements address challenges such as memory limitations, noisy data, and the computational cost of processing large sequences, leading to faster and more efficient models for various applications including natural language processing and video recognition. The impact is seen in improved model speed, reduced energy consumption, and enhanced performance on downstream tasks.