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npx versuz@latest install hiyenwong-ai-collection-collection-skills-kernel-associative-memory-quantizationgit clone https://github.com/hiyenwong/ai_collection.gitcp ai_collection/SKILL.MD ~/.claude/skills/hiyenwong-ai-collection-collection-skills-kernel-associative-memory-quantization/SKILL.md--- name: kernel-associative-memory-quantization description: "High-capacity Hopfield associative memories via Kernel Logistic Regression with striking quantization robustness but pruning sensitivity. Sparse function dense representation principle. Activation: associative memory, Hopfield network, quantization, kernel logistic regression." --- # Quantization robustness from dense representations of sparse functions in high-complexity linear codes > arXiv:2604.20333 — Akira Tamamori ## Metadata - **Source**: arXiv:2604.20333 - **Authors**: Akira Tamamori - **Published**: 2025-04 - **Relevance**: medium - **URL**: https://arxiv.org/abs/2604.20333 ## Core Methodology ### Key Innovation High-capacity associative memories based on Kernel Logistic Regression (KLR) are known for their exceptional performance but are hindered by high computational costs. This paper investigates the compressibility of KLR-trained Hopfield networks to understand the geometric principles of its robust encoding. We provide a comprehensive geometric theory based on spontaneous symmetry breaking and Walsh analysis, and validate it with compression experiments (quantization and pruning). Our experiments r ### Technical Framework eveal a striking contrast: the network is extremely robust to low-precision quantization but highly sensitive to pruning. Our theory explains this via a ``sparse function, dense representation'' principle, where a sparse input mapping is implemented with a dense, bimodal parameterization. Our findings not only provide a practical path to hardware-efficient kernel memories but also offer new insights into the geometric principles of robust representation in neural systems. ## Implementation Guide ### Prerequisites - Python environment with scientific computing libraries - Access to paper's supplementary materials at https://arxiv.org/abs/2604.20333 ### Step-by-Step 1. Read the full paper at https://arxiv.org/abs/2604.20333 2. Identify the core algorithm/framework from the methodology section 3. Implement the key components as described in the paper 4. Validate using the paper's reported benchmarks ## Applications - Neuroscience research - Computational neuroscience - Neural network design and optimization ## Pitfalls - Results may be preliminary (preprint) - Reproducibility depends on availability of code/data ## Related Skills - computational-neuroscience-models - neural-population-dynamics - spiking-neural-network-training