A Gaussian process surrogate gate inserted between generative crystal models and property oracles matches or exceeds ungated fine-tuning while using roughly one-fifth the oracle calls for heat capacity and bulk modulus.
Bayesian optimization
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
verdicts
UNVERDICTED 3representative citing papers
AtomTreeSearch embeds a neutral-atom quantum MWIS subroutine inside Monte Carlo Tree Search and matches or exceeds OR-Tools and simulated annealing on TSP instances up to 100 cities.
A joint optimization of neural manifold learning and active-learning-guided Gaussian process regression in latent space outperforms random sampling on synthetic data for complex functions.
citing papers explorer
-
Surrogate-Gated Generation and Foundation-Model Embeddings for Bayesian Materials Design
A Gaussian process surrogate gate inserted between generative crystal models and property oracles matches or exceeds ungated fine-tuning while using roughly one-fifth the oracle calls for heat capacity and bulk modulus.
-
Quantum-enhanced Monte Carlo Tree Search framework for combinatorial optimization problems
AtomTreeSearch embeds a neutral-atom quantum MWIS subroutine inside Monte Carlo Tree Search and matches or exceeds OR-Tools and simulated annealing on TSP instances up to 100 cities.
-
Active Learning for Manifold Gaussian Process Regression
A joint optimization of neural manifold learning and active-learning-guided Gaussian process regression in latent space outperforms random sampling on synthetic data for complex functions.