Unified Neural Scaling Laws
Unified Neural Scaling Laws proposes a unified neural network scaling law that applies to various neural architectures, including CNN, RNN, and Transformer. The law reveals the relationship between neural network performance and parameter quantity, providing a theoretical basis for model design and optimization.
入选理由:Unified Neural Scaling Laws 提出了一种统一的神经网络缩放定律,适用于多种神经架构。