18 citations · 50 across the 9 of their papers we have counts for
9 papers
Towards modeling evolving longitudinal health trajectories with a transformer-based deep learning model
Hans Moen, Vishnu Raj, Andrius Vabalas +4
Health registers contain rich information about individuals' health histories. Here our interest lies in understanding how individuals' health trajectories evolve in a nationwide l…
Incorporating functional summary information in Bayesian neural networks using a Dirichlet process likelihood approach
Vishnu Raj, Tianyu Cui, Markus Heinonen +1
Bayesian neural networks (BNNs) can account for both aleatoric and epistemic uncertainty. However, in BNNs the priors are often specified over the weights which rarely reflects tru…
Deconfounded Representation Similarity for Comparison of Neural Networks
Tianyu Cui, Yogesh Kumar, Pekka Marttinen +1
Similarity metrics such as representational similarity analysis (RSA) and centered kernel alignment (CKA) have been used to compare layer-wise representations between neural networ…
Informative Bayesian Neural Network Priors for Weak Signals
Tianyu Cui, Aki Havulinna, Pekka Marttinen +1
Encoding domain knowledge into the prior over the high-dimensional weight space of a neural network is challenging but essential in applications with limited data and weak signals.…
Learning Global Pairwise Interactions with Bayesian Neural Networks
Tianyu Cui, Pekka Marttinen, Samuel Kaski
Estimating global pairwise interaction effects, i.e., the difference between the joint effect and the sum of marginal effects of two input features, with uncertainty properly quant…
Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation
Iiris Sundin, Tomi Peltola, Muntasir Mamun Majumder +6
Predicting the efficacy of a drug for a given individual, using high-dimensional genomic measurements, is at the core of precision medicine. However, identifying features on which…
Interactive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets
Luana Micallef, Iiris Sundin, Pekka Marttinen +5
Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can impr…
Multiple Output Regression with Latent Noise
Jussi Gillberg, Pekka Marttinen, Matti Pirinen +7
In high-dimensional data, structured noise caused by observed and unobserved factors affecting multiple target variables simultaneously, imposes a serious challenge for modeling, b…
Bayesian Information Sharing Between Noise And Regression Models Improves Prediction of Weak Effects
Jussi Gillberg, Pekka Marttinen, Matti Pirinen +5
We consider the prediction of weak effects in a multiple-output regression setup, when covariates are expected to explain a small amount, less than , of the variance of…