2.6k citations · 11.1k across the 96 of their papers we have counts for
12 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…
Probabilistic Meta-Representations Of Neural Networks
Theofanis Karaletsos, Peter Dayan, Zoubin Ghahramani
Existing Bayesian treatments of neural networks are typically characterized by weak prior and approximate posterior distributions according to which all the weights are drawn indep…
Antithetic and Monte Carlo kernel estimators for partial rankings
Maria Lomeli, Mark Rowland, Arthur Gretton +1
In the modern age, rankings data is ubiquitous and it is useful for a variety of applications such as recommender systems, multi-object tracking and preference learning. However, m…
Variational Bayesian dropout: pitfalls and fixes
Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani
Dropout, a stochastic regularisation technique for training of neural networks, has recently been reinterpreted as a specific type of approximate inference algorithm for Bayesian n…
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…
Handling Incomplete Heterogeneous Data using VAEs
Alfredo Nazabal, Pablo M. Olmos, Zoubin Ghahramani +1
Variational autoencoders (VAEs), as well as other generative models, have been shown to be efficient and accurate for capturing the latent structure of vast amounts of complex high…