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Stochastic Differential Equations with Variational Wishart Diffusions
We present a Bayesian non-parametric way of inferring stochastic differential equations for both regression tasks and continuous-time …
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Variational Integrator Networks for Physically Structured Embeddings
Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. …
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Asynchronous Gibbs Sampling
Gibbs sampling is a Markov Chain Monte Carlo (MCMC) method often used in Bayesian learning. It is widely believed that MCMC methods are …
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Active Sampling for Pairwise Comparisons via Approximate Message Passing and Information Gain Maximization
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Contrast Sensitivity Functions for HDR Displays
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Deep learning for monthly Arctic sea ice concentration prediction
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Predicting Engagement in Video Lectures
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SUM'20: State-based User Modelling
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Towards self-certified learning: Probabilistic neural networks trained by PAC-Bayes with Backprop
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TrueLearn: A Family of Bayesian Algorithms to Match Lifelong Learners to Open Educational Resources
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