Sparse expert models leverage the concept of sparsity within neural networks, allowing for dynamic parameter swapping. By utilizing a router network to direct inputs to a select few experts, these models maintain efficiency while ensuring uniform utilization during training. This approach is akin to managing a large dense matrix with numerous zeroed-out weights, optimizing performance in complex tasks.
