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Found 7 papers, 3 papers with code
BaCO: A Fast and Portable Bayesian Compiler Optimization Framework
We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose autotuner for modern compilers targeting CPUs, GPUs, and FPGAs.
$π$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
To address this issue, we propose $\pi$BO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user.
$\pi$BO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization
To address this issue, we propose $\pi$BO, an acquisition function generalization which incorporates prior beliefs about the location of the optimum in the form of a probability distribution, provided by the user.
Prior-guided Bayesian Optimization
While Bayesian Optimization (BO) is a very popular method for optimizing expensive black-box functions, it fails to leverage the experience of domain experts.
Bayesian Optimization with a Prior for the Optimum
We show that BOPrO is around 6. 67x faster than state-of-the-art methods on a common suite of benchmarks, and achieves a new state-of-the-art performance on a real-world hardware design application.
DeepFreak: Learning Crystallography Diffraction Patterns with Automated Machine Learning
In this paper, we introduce a new serial crystallography dataset comprised of real and synthetic images; the synthetic images are generated through the use of a simulator that is both scalable and accurate.
DiffraNet: Automatic Classification of Serial Crystallography Diffraction Patterns
In this paper, we introduce a new serial crystallography dataset generated through the use of a simulator; the synthetic images are labeled and they are both scalable and accurate.
