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Arxiv
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GradMetaNet: An Equivariant Architecture for Learning on Gradients
- Practitioners often treat gradients of neural networks as inputs to task-specific algorithms for optimization, editing, and analysis.
- A new paper introduces GradMetaNet, an architecture designed specifically for processing gradients by following principles like equivariant design and efficient gradient representation.
- GradMetaNet is demonstrated to outperform previous approaches in approximating natural gradient-based functions for tasks like learned optimization, INR editing, and loss landscape curvature estimation.
- The architecture, based on simple equivariant blocks, is proven to be universal and effective on a variety of gradient-based tasks involving MLPs and transformers.
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