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11 papers · 2 filters
A Statistical Approach to Assessing Neural Network Robustness
Stefan Webb, Tom Rainforth, Yee Whye Teh +1
We present a new approach to assessing the robustness of neural networks based on estimating the proportion of inputs for which a property is violated. Specifically, we estimate th…
Semi-unsupervised Learning of Human Activity using Deep Generative Models
Matthew Willetts, Aiden Doherty, Stephen Roberts +1
We introduce 'semi-unsupervised learning', a problem regime related to transfer learning and zero-shot learning where, in the training data, some classes are sparsely labelled and…
Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning
Bryan Lim, Mihaela van der Schaar
Joint models for longitudinal and time-to-event data are commonly used in longitudinal studies to forecast disease trajectories over time. Despite the many advantages of joint mode…
Gaussian mixture models with Wasserstein distance
Benoit Gaujac, Ilya Feige, David Barber
Generative models with both discrete and continuous latent variables are highly motivated by the structure of many real-world data sets. They present, however, subtleties in traini…
A Stein variational Newton method
Gianluca Detommaso, Tiangang Cui, Alessio Spantini +2
Stein variational gradient descent (SVGD) was recently proposed as a general purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]: it minimizes the Kullbac…
Efficient Inference in Multi-task Cox Process Models
Virginia Aglietti, Theodoros Damoulas, Edwin Bonilla
We generalize the log Gaussian Cox process (LGCP) framework to model multiple correlated point data jointly. The observations are treated as realizations of multiple LGCPs, whose l…