Layerwise self-supervised local learning rules for deep networks on the Random Hierarchy Model (RHM). Direct feedback approximations fail due to input-specific masking nonlinearity; layerwise contrastive/non-contrastive self-supervised rules succeed and match backprop data efficiency while being cortex-compatible. Use when: designing biologically plausible learning rules, local learning algorithms
no risky patterns detected
Type: Agent skill (SKILL.md) · Category: Science, Health & Education · Source: https://github.com/hiyenwong/ai_collection/tree/HEAD/collection/skills/ai-ml/sel
| Capabilities (fewer powerful permissions = higher) | 50/50 |
| Maintenance | 21/35 |
| Documentation | 15/15 |
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