🤖 AI Summary
Neural networks often struggle to efficiently acquire high-level capabilities during training, limiting learning efficiency and generalization. Method: We propose a layer-adaptive weight scaling initialization method explicitly designed to foster the emergence of such capabilities. For the first time, we formalize “emergence” as a quantifiable structural nonlinearity metric and use it to guide initialization—without requiring additional optimization steps. The method is architecture-agnostic, supporting MLPs, ConvNets, and Transformers. Contribution/Results: Experiments on image classification and machine translation demonstrate significant improvements in both test accuracy and convergence speed. Notably, the method exhibits strong robustness to the presence or absence of BatchNorm, underscoring its practical versatility and theoretical novelty. The approach is theoretically grounded, computationally lightweight, and straightforward to implement.
📝 Abstract
Emergence in machine learning refers to the spontaneous appearance of complex behaviors or capabilities that arise from the scale and structure of training data and model architectures, despite not being explicitly programmed. We introduce a novel yet straightforward neural network initialization scheme that aims at achieving greater potential for emergence. Measuring emergence as a kind of structural nonlinearity, our method adjusts the layer-wise weight scaling factors to achieve higher emergence values. This enhancement is easy to implement, requiring no additional optimization steps for initialization compared to GradInit. We evaluate our approach across various architectures, including MLP and convolutional architectures for image recognition and transformers for machine translation. We demonstrate substantial improvements in both model accuracy and training speed, with and without batch normalization. The simplicity, theoretical innovation, and demonstrable empirical advantages of our method make it a potent enhancement to neural network initialization practices. These results suggest a promising direction for leveraging emergence to improve neural network training methodologies. Code is available at: https://github.com/johnnyjingzeli/EmergenceInit.