3 citations · 3 across the 1 of their papers we have counts for
5 papers
Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort +5
Recent approaches to efficiently ensemble neural networks have shown that strong robustness and uncertainty performance can be achieved with a negligible gain in parameters over th…
Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding
Gergely Flamich, Marton Havasi, José Miguel Hernández-Lobato
Variational Autoencoders (VAEs) have seen widespread use in learned image compression. They are used to learn expressive latent representations on which downstream compression meth…
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
Marton Havasi, Robert Peharz, José Miguel Hernández-Lobato
While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage re…
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes
Deep Gaussian Processes (DGPs) are hierarchical generalizations of Gaussian Processes that combine well calibrated uncertainty estimates with the high flexibility of multilayer mod…
Deep Gaussian Processes with Decoupled Inducing Inputs
Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes
Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussian Processes (GP) that have proven to work effectively on a multiple supervised regression tasks. They combi…