Making AI models leaner and faster without sacrificing accuracy

Google researchers developed Sequential Attention, a new technique that makes AI models smaller and faster by intelligently selecting which features or components to keep during training. Unlike traditional methods that evaluate all features simultaneously, this approach uses a step-by-step greedy selection process that adapts based on previous choices, successfully reducing model size while maintaining accuracy across various neural network tasks.