ArXiv TLDR

Akira Tamamori

3 papers · Latest:

Neural & Evolutionary Computing

Efficient event-driven retrieval in high-capacity kernel Hopfield networks

This paper shows that asynchronous KLR Hopfield networks achieve high capacity and efficient event-driven retrieval, suitable for neuromorphic hardware.

2605.05978
Neural & Evolutionary Computing

Geometric analysis of attractor boundaries and storage capacity limits in kernel Hopfield networks

This paper investigates KLR Hopfield networks, revealing high storage capacity (P/N ~20) and that dynamical stability, not geometry, limits their ultimate storage.

2605.00366
Neural & Evolutionary Computing

Quantization robustness from dense representations of sparse functions in high-capacity kernel associative memory

This paper reveals that high-capacity kernel memories are robust to quantization but sensitive to pruning due to a "sparse function, dense representation" principle.

2604.20333

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