Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space

TMLR Paper6571 Authors

Abstract: The rapid advancements in using neural networks as implicit data representations have attracted significant interest in developing machine learning methods that analyze and process the weight spaces of other neural networks. However, efficiently handling these high-dimensional weight spaces remains challenging. Existing methods often overlook the sequential nature of layer-by-layer processing in neural network inference. In this work, we propose a novel approach using dynamic graphs to represent neural network parameters, capturing the temporal dynamics of inference. Our Dynamic Neural Graph Encoder (DNG-Encoder) processes these graphs, preserving the sequential nature of neural processing. Additionally, we also leverage DNG-Encoder to develop INR2JLS for facilitate downstream applications, such as classifying INRs. Our approach demonstrates significant improvements across multiple tasks, surpassing the state-of-the-art INR classification accuracy by approximately 10% on the CIFAR-100-INR. The source code has been made available in the supplementary materials.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Lechao_Xiao2

Submission Number: 6571