32 citations · 172 across the 28 of their papers we have counts for
5 papers · 1 filter
Efficient and Robust Machine Learning for Real-World Systems
Franz Pernkopf, Wolfgang Roth, Matthias Zoehrer +7
While machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the Internet-of-Things fuel the interest in resource ef…
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…
Learning Deep Mixtures of Gaussian Process Experts Using Sum-Product Networks
Martin Trapp, Robert Peharz, Carl E. Rasmussen +1
While Gaussian processes (GPs) are the method of choice for regression tasks, they also come with practical difficulties, as inference cost scales cubic in time and quadratic in me…
Automatic Bayesian Density Analysis
Antonio Vergari, Alejandro Molina, Robert Peharz +3
Making sense of a dataset in an automatic and unsupervised fashion is a challenging problem in statistics and AI. Classical approaches for {exploratory data analysis} are usually n…
Probabilistic Deep Learning using Random Sum-Product Networks
Robert Peharz, Antonio Vergari, Karl Stelzner +4
The need for consistent treatment of uncertainty has recently triggered increased interest in probabilistic deep learning methods. However, most current approaches have severe limi…