Enhanced Log-GP
Cross-source consensus on Enhanced Log-GP from 1 sources and 5 claims.
1 sources · 5 claims
How it works
Comparisons
Highlighted claims
- The agnostic phase uses Gaussian-process regression in log-intensity space for neutron spectroscopy reconstruction. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- The enhanced Log-GP policy adds initialization, variance weighting, consumed-area exclusion, and an energy taper to the base approach. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- Agnostic methods are better suited than physics-only TAS-AI for unknown global response mapping in blind reconstruction benchmarks. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- Enhanced Log-GP was competitive with grid sampling in the PySpinW gapless case and best in the gapped case, while physics-only TAS-AI performed poorly for blind whole-window mapping. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
- Enhanced Log-GP has higher benchmark-harness planner-side costs than TAS-AI physics planning. — Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy