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Found 7 papers, 3 papers with code
Warm Start Marginal Likelihood Optimisation for Iterative Gaussian Processes
Gaussian processes are a versatile probabilistic machine learning model whose effectiveness often depends on good hyperparameters, which are typically learned by maximising the marginal likelihood.
Improving Linear System Solvers for Hyperparameter Optimisation in Iterative Gaussian Processes
Scaling hyperparameter optimisation to very large datasets remains an open problem in the Gaussian process community.
Denoising Diffusion Probabilistic Models in Six Simple Steps
Denoising Diffusion Probabilistic Models (DDPMs) are a very popular class of deep generative model that have been successfully applied to a diverse range of problems including image and video generation, protein and material synthesis, weather forecasting, and neural surrogates of partial differential equations.
Implicit meta-learning may lead language models to trust more reliable sources
Fine-tuning on this dataset leads to implicit meta-learning (IML): in further fine-tuning, the model updates to make more use of text that is tagged as useful.
Hyperparameter Optimization through Neural Network Partitioning
Well-tuned hyperparameters are crucial for obtaining good generalization behavior in neural networks.
Timewarp: Transferable Acceleration of Molecular Dynamics by Learning Time-Coarsened Dynamics
Molecular dynamics (MD) simulation is a widely used technique to simulate molecular systems, most commonly at the all-atom resolution where equations of motion are integrated with timesteps on the order of femtoseconds ($1\textrm{fs}=10^{-15}\textrm{s}$).
Ensemble Distribution Distillation
The properties of EnD$^2$ are investigated on both an artificial dataset, and on the CIFAR-10, CIFAR-100 and TinyImageNet datasets, where it is shown that EnD$^2$ can approach the classification performance of an ensemble, and outperforms both standard DNNs and Ensemble Distillation on the tasks of misclassification and out-of-distribution input detection.
