Search Results for author:
Found 3 papers, 1 papers with code
Neural Bootstrapping Attention for Neural Processes
Recently, Bootstrapping (Attentive) Neural Processes (B(A)NP) propose a bootstrap method to capture the functional uncertainty which can replace the latent variable in (Attentive) Neural Processes ((A)NP), thus overcoming the limitations of Gaussian assumption on the latent variable.
Neural Bootstrapper
Bootstrapping has been a primary tool for ensemble and uncertainty quantification in machine learning and statistics.
Generative Parameter Sampler For Scalable Uncertainty Quantification
Uncertainty quantification has been a core of the statistical machine learning, but its computational bottleneck has been a serious challenge for both Bayesians and frequentists.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
