107 citations · 111 across the 2 of their papers we have counts for
8 papers
User-Dependent Neural Sequence Models for Continuous-Time Event Data
Alex Boyd, Robert Bamler, Stephan Mandt +1
Continuous-time event data are common in applications such as individual behavior data, financial transactions, and medical health records. Modeling such data can be very challengi…
Scalable Gaussian Process Variational Autoencoders
Metod Jazbec, Matthew Ashman, Vincent Fortuin +3
Conventional variational autoencoders fail in modeling correlations between data points due to their use of factorized priors. Amortized Gaussian process inference through GP-VAEs…
Variational Dynamic Mixtures
Chen Qiu, Stephan Mandt, Maja Rudolph
Deep probabilistic time series forecasting models have become an integral part of machine learning. While several powerful generative models have been proposed, we provide evidence…
Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion
Fabian Jirasek, Rodrigo A. S. Alves, Julie Damay +6
Activity coefficients, which are a measure of the non-ideality of liquid mixtures, are a key property in chemical engineering with relevance to modeling chemical and phase equilibr…
GP-VAE: Deep Probabilistic Time Series Imputation
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch +1
Multivariate time series with missing values are common in areas such as healthcare and finance, and have grown in number and complexity over the years. This raises the question wh…
Quasi-Monte Carlo Variational Inference
Alexander Buchholz, Florian Wenzel, Stephan Mandt
Many machine learning problems involve Monte Carlo gradient estimators. As a prominent example, we focus on Monte Carlo variational inference (MCVI) in this paper. The performance…