Dynamic Neural Graph: Facilitating Temporal Dynamics Learning in Deep Weight Space

Di Wu, Huan Liu, Zhixiang Chi, YUANHAO YU, Konstantinos N Plataniotis, Yang Wang

Keywords: Dynamic Graph neural networks, Deep weight space, Implicit neural representations, Networks for networks, Neural graphs

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.

Primary Area: learning on graphs and other geometries & topologies

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Submission Number: 5495