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Found 2 papers, 1 papers with code
Unleashing the Potential of Acquisition Functions in High-Dimensional Bayesian Optimization
Bayesian optimization (BO) is widely used to optimize expensive-to-evaluate black-box functions. BO first builds a surrogate model to represent the objective function and assesses its uncertainty.
Optimizing Sparse Matrix Multiplications for Graph Neural Networks
Our model is first trained offline using training matrix samples, and the trained model can be applied to any input matrix and GNN kernels with SpMM computation.
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