arXiv:2605.28007v1 Announce Type: new Abstract: Deep networks are powerful function approximators, but they typically store many different computations in shared weight matrices, making it difficult to selectively reuse or adapt parts of them when a familiar structure appears in novel combinations. We introduce the Vector Network (VN), a hierarchical recurrent architecture in which each layer replaces a fixed weight matrix with a library of reusable rank-1 weight atoms. For each input, VN minimizes a layer-local energy to infer a sparse set of active weight atoms and their coefficients, jointl
The paper introduces a novel deep learning architecture that addresses a core limitation of current neural networks, enabling more efficient and adaptable AI systems, reflecting a continuous push for more robust AI foundations.
This research could lead to more efficient, adaptable, and generalizable AI models by improving how deep networks reuse and adapt learned information, impacting future AI development and application.
The proposed Vector Network fundamentally alters how deep networks store and access computations, potentially enabling AI to handle novel combinations of familiar structures with greater flexibility than current models.
- · AI researchers
- · Machine learning hardware developers
- · Software developers (AI-driven applications)
- · Traditional deep learning architectures (by comparison)
Vector Networks enable more efficient and adaptable AI models through their compositional latent structure.
Improved AI adaptability could accelerate the development of more complex autonomous systems and agents.
These advancements might contribute to the broader viability and deployment of AI in highly dynamic environments that require continuous learning and adaptation.
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Read at arXiv cs.LG