Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
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 highdimensional 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 En
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- Linked via arxiv authorDi Wu →
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
- Linked via arxiv authorHuan Liu →
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
- Linked via arxiv authorZhixiang Chi →
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
- Linked via arxiv authorYuanhao Yu →
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
- Linked via arxiv authorKonstantinos N. Plataniotis →
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space
- Linked via arxiv authorYang Wang →
Dynamic Neural Graph Encoding of Inference Processes in Deep Weight Space