49 citations · 156 across the 29 of their papers we have counts for
10 papers · 1 filter
Identifying latent distances with Finslerian geometry
Alison Pouplin, David Eklund, Carl Henrik Ek +1
Riemannian geometry provides us with powerful tools to explore the latent space of generative models while preserving the underlying structure of the data. The latent space can be…
Bayesian nonparametric shared multi-sequence time series segmentation
Olga Mikheeva, Ieva Kazlauskaite, Hedvig Kjellström +1
In this paper, we introduce a method for segmenting time series data using tools from Bayesian nonparametrics. We consider the task of temporal segmentation of a set of time series…
Interpretable Dynamics Models for Data-Efficient Reinforcement Learning
Markus Kaiser, Clemens Otte, Thomas Runkler +1
In this paper, we present a Bayesian view on model-based reinforcement learning. We use expert knowledge to impose structure on the transition model and present an efficient learni…
Diagnostic Prediction Using Discomfort Drawings
Cheng Zhang, Hedvig Kjellstrom, Bo C. Bertilson
In this paper, we explore the possibility to apply machine learning to make diagnostic predictions using discomfort drawings. A discomfort drawing is an intuitive way for patients…
Diagnostic Prediction Using Discomfort Drawings with IBTM
Cheng Zhang, Hedvig Kjellstrom, Carl Henrik Ek +1
In this paper, we explore the possibility to apply machine learning to make diagnostic predictions using discomfort drawings. A discomfort drawing is an intuitive way for patients…
Unsupervised Learning with Imbalanced Data via Structure Consolidation Latent Variable Model
Fariba Yousefi, Zhenwen Dai, Carl Henrik Ek +1
Unsupervised learning on imbalanced data is challenging because, when given imbalanced data, current model is often dominated by the major category and ignores the categories with…