6 papers
Multilevel Gibbs Sampling for Bayesian Regression
Joris Tavernier, Jaak Simm, Adam Arany +2
Bayesian regression remains a simple but effective tool based on Bayesian inference techniques. For large-scale applications, with complicated posterior distributions, Markov Chain…
ChemGrapher: Optical Graph Recognition of Chemical Compounds by Deep Learning
Martijn Oldenhof, Adam Arany, Yves Moreau +1
In drug discovery, knowledge of the graph structure of chemical compounds is essential. Many thousands of scientific articles in chemistry and pharmaceutical sciences have investig…
Expressive Graph Informer Networks
Jaak Simm, Adam Arany, Edward De Brouwer +1
Applying machine learning to molecules is challenging because of their natural representation as graphs rather than vectors.Several architectures have been recently proposed for de…
GRU-ODE-Bayes: Continuous modeling of sporadically-observed time series
Edward De Brouwer, Jaak Simm, Adam Arany +1
Modeling real-world multidimensional time series can be particularly challenging when these are sporadically observed (i.e., sampling is irregular both in time and across dimension…
SMURFF: a High-Performance Framework for Matrix Factorization
Tom Vander Aa, Imen Chakroun, Thomas J. Ashby +10
Bayesian Matrix Factorization (BMF) is a powerful technique for recommender systems because it produces good results and is relatively robust against overfitting. Yet BMF is more c…
Deep Ensemble Tensor Factorization for Longitudinal Patient Trajectories Classification
Edward De Brouwer, Jaak Simm, Adam Arany +1
We present a generative approach to classify scarcely observed longitudinal patient trajectories. The available time series are represented as tensors and factorized using generati…