3 papers
cs.LG2022
Entry Dependent Expert Selection in Distributed Gaussian Processes Using Multilabel Classification
Hamed Jalali, Gjergji Kasneci
By distributing the training process, local approximation reduces the cost of the standard Gaussian Process. An ensemble technique combines local predictions from Gaussian experts…
cs.LG2022
Gaussian Graphical Models as an Ensemble Method for Distributed Gaussian Processes
Hamed Jalali, Gjergji Kasneci
Distributed Gaussian process (DGP) is a popular approach to scale GP to big data which divides the training data into some subsets, performs local inference for each partition, and…
cs.LG2021
A Robust Unsupervised Ensemble of Feature-Based Explanations using Restricted Boltzmann Machines
Vadim Borisov, Johannes Meier, Johan van den Heuvel +2
Understanding the results of deep neural networks is an essential step towards wider acceptance of deep learning algorithms. Many approaches address the issue of interpreting artif…