2.6k citations · 11.1k across the 96 of their papers we have counts for
8 papers · 2 filters
Variational Gaussian Dropout is not Bayesian
Jiri Hron, Alexander G. de G. Matthews, Zoubin Ghahramani
Gaussian multiplicative noise is commonly used as a stochastic regularisation technique in training of deterministic neural networks. A recent paper reinterpreted the technique as…
General Latent Feature Modeling for Data Exploration Tasks
Isabel Valera, Melanie F. Pradier, Zoubin Ghahramani
This paper introduces a general Bayesian non- parametric latent feature model suitable to per- form automatic exploratory analysis of heterogeneous datasets, where the attributes d…
Improving Output Uncertainty Estimation and Generalization in Deep Learning via Neural Network Gaussian Processes
Tomoharu Iwata, Zoubin Ghahramani
We propose a simple method that combines neural networks and Gaussian processes. The proposed method can estimate the uncertainty of outputs and flexibly adjust target functions wh…
One-Shot Learning in Discriminative Neural Networks
Jordan Burgess, James Robert Lloyd, Zoubin Ghahramani
We consider the task of one-shot learning of visual categories. In this paper we explore a Bayesian procedure for updating a pretrained convnet to classify a novel image category f…
Adversarial Examples, Uncertainty, and Transfer Testing Robustness in Gaussian Process Hybrid Deep Networks
John Bradshaw, Alexander G. de G. Matthews, Zoubin Ghahramani
Deep neural networks (DNNs) have excellent representative power and are state of the art classifiers on many tasks. However, they often do not capture their own uncertainties well…
Lost Relatives of the Gumbel Trick
Matej Balog, Nilesh Tripuraneni, Zoubin Ghahramani +1
The Gumbel trick is a method to sample from a discrete probability distribution, or to estimate its normalizing partition function. The method relies on repeatedly applying a rando…