DAGGER: Gradient-Free Construction of Transiently Amplifying Networks under Hard Connectivity Constraints
arXiv:2606.01227v1 Announce Type: new Abstract: Many networks not only support but also rely on transient non-normal amplification, an orders-of-magnitude increase in the activity of an otherwise stable system. Constructing such networks under hard sign/sparsity/diagonal constraints -- the regime relevant for biological connectomes and structured RNN initializations -- has so far required either gradient-based local search with thousands of inner-loop eigendecompositions or Schur-form direct construction in an abstract basis that breaks the constraints under projection. Here we introduce DAGGE
The paper provides a novel, gradient-free method for constructing complex, transiently amplifying networks, addressing a long-standing challenge in AI and neurobiology research.
This breakthrough could significantly accelerate the development of more biologically plausible and efficient AI and lead to better understanding of neural networks relevant to structured RNNs and biological connectomes.
The ability to construct complex networks with desired transient amplification properties without gradient-based methods or abstract bases fundamentally changes how such systems can be designed and analyzed.
- · AI researchers
- · Deep learning developers
- · Computational neuroscientists
- · Biomedical engineering
- · Developers reliant solely on gradient-based optimization
More efficient and robust AI models, especially recurrent neural networks, can be developed sooner.
Improved understanding and modeling of brain function, potentially leading to advances in Neuromorphic computing.
New classes of AI architectures emerge which are more aligned with biological principles, potentially unlocking greater capabilities.
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Read at arXiv cs.LG