Sequential Attention: Making AI models leaner and faster without sacrificing accuracy

Google Research introduced Sequential Attention as a subset selection algorithm for making large scale machine learning models more efficient. The algorithm uses a greedy mechanism to sequentially select the best next component to add to the model during a single training process. This integration allows application to large models with minimal overhead. The method addresses the NP hard nature of feature selection Sequential Attention achieved state of the art results across neural network benchmarks and proved mathematically equivalent to the Orthogonal Matching Pursuit algorithm in linear regression cases. An extension called SequentialAttention++ applies the framework to structured neural network pruning Applications include optimizing feature embedding layers in large embedding models for recommender systems. The researchers said the technique provides quality gains and efficiency savings in these models.