High-Dimensional Limitations
Cross-source consensus on High-Dimensional Limitations from 1 sources and 4 claims.
1 sources · 4 claims
Uses
Risks & contraindications
Highlighted claims
- The current SHAPE implementation struggles in high-dimensional spaces. — When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
- Compressed memory can still suffer serious information loss in high-dimensional settings. — When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
- Future work includes scalable high-dimensional memory and decomposition. — When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize
- Some constants and bounds may deteriorate rapidly with dimension. — When Descent Is Too Stable: Event-Triggered Hamiltonian Learning to Optimize