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Found 4 papers, 2 papers with code
Model-based Asynchronous Hyperparameter and Neural Architecture Search
We introduce a model-based asynchronous multi-fidelity method for hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization.
BORE: Bayesian Optimization by Density-Ratio Estimation
Bayesian optimization (BO) is among the most effective and widely-used blackbox optimization methods.
Cycle-Consistent Adversarial Learning as Approximate Bayesian Inference
We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent variable model (LVM), where one seeks the underlying hidden representations of entities from one domain as entities from the other domain.
Spherical Inducing Features for Orthogonally-Decoupled Gaussian Processes
Despite their many desirable properties, Gaussian processes (GPs) are often compared unfavorably to deep neural networks (NNs) for lacking the ability to learn representations.
