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Found 7 papers, 3 papers with code
Efficient Bayesian Optimization with Deep Kernel Learning and Transformer Pre-trained on Multiple Heterogeneous Datasets
Bayesian optimization (BO) is widely adopted in black-box optimization problems and it relies on a surrogate model to approximate the black-box response function.
Exploring Effective Mask Sampling Modeling for Neural Image Compression
Specifically, Cube Mask Sampling Module (CMSM) is proposed to apply both spatial and channel mask sampling modeling to image compression in the pre-training stage.
Reweighted Interacting Langevin Diffusions: an Accelerated Sampling Methodfor Optimization
We proposed a new technique to accelerate sampling methods for solving difficult optimization problems.
High-Dimensional Bayesian Optimisation with Variational Autoencoders and Deep Metric Learning
We introduce a method combining variational autoencoders (VAEs) and deep metric learning to perform Bayesian optimisation (BO) over high-dimensional and structured input spaces.
Ranked #1 on
Molecular Graph Generation
on ZINC
HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
Our results on the Bayesmark benchmark indicate that heteroscedasticity and non-stationarity pose significant challenges for black-box optimisers.
Ranked #1 on
Hyperparameter Optimization
on Bayesmark
Bayesian Optimization Approach for Analog Circuit Synthesis Using Neural Network
Bayesian optimization with Gaussian process as surrogate model has been successfully applied to analog circuit synthesis.
Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design
In each iteration, the multi-objective optimization of the multiple acquisition functions is performed to search for the Pareto front of the acquisition functions.
