Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation

• URL: http://arxiv.org/abs/2412.15554v3
• Date: Sun, 19 Jan 2025 02:54:49 GMT
• Title: Architecture-Aware Learning Curve Extrapolation via Graph Ordinary Differential Equation
• Authors: Yanna Ding, Zijie Huang, Xiao Shou, Yihang Guo, Yizhou Sun, Jianxi Gao,
• Abstract summary: We propose a novel architecture-aware neural differential equation model to forecast learning curves continuously. Our model outperforms current state-of-the-art learning curve methods and extrapolation approaches for both pure time-series modeling and CNN-based learning curves.
• Score: 33.63030304318472
• License:
• Abstract: Learning curve extrapolation predicts neural network performance from early training epochs and has been applied to accelerate AutoML, facilitating hyperparameter tuning and neural architecture search. However, existing methods typically model the evolution of learning curves in isolation, neglecting the impact of neural network (NN) architectures, which influence the loss landscape and learning trajectories. In this work, we explore whether incorporating neural network architecture improves learning curve modeling and how to effectively integrate this architectural information. Motivated by the dynamical system view of optimization, we propose a novel architecture-aware neural differential equation model to forecast learning curves continuously. We empirically demonstrate its ability to capture the general trend of fluctuating learning curves while quantifying uncertainty through variational parameters. Our model outperforms current state-of-the-art learning curve extrapolation methods and pure time-series modeling approaches for both MLP and CNN-based learning curves. Additionally, we explore the applicability of our method in Neural Architecture Search scenarios, such as training configuration ranking.

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