4 citations · 8 across the 5 of their papers we have counts for
7 papers
Reversible Genetically Modified Mode Jumping MCMC
Aliaksandr Hubin, Florian Frommlet, Geir Storvik
In this paper, we introduce a reversible version of a genetically modified mode jumping Markov chain Monte Carlo algorithm (GMJMCMC) for inference on posterior model probabilities…
skweak: Weak Supervision Made Easy for NLP
Pierre Lison, Jeremy Barnes, Aliaksandr Hubin
We present skweak, a versatile, Python-based software toolkit enabling NLP developers to apply weak supervision to a wide range of NLP tasks. Weak supervision is an emerging machin…
Named Entity Recognition without Labelled Data: A Weak Supervision Approach
Pierre Lison, Aliaksandr Hubin, Jeremy Barnes +1
Named Entity Recognition (NER) performance often degrades rapidly when applied to target domains that differ from the texts observed during training. When in-domain labelled data i…
A Bayesian binomial regression model with latent Gaussian processes for modelling DNA methylation
Aliaksandr Hubin, Geir O Storvik, Paul E Grini +1
Epigenetic observations are represented by the total number of reads from a given pool of cells and the number of methylated reads, making it reasonable to model this data by a bin…
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov models
Aliaksandr Hubin
Non-homogeneous hidden Markov models (NHHMM) are a subclass of dependent mixture models used for semi-supervised learning, where both transition probabilities between the latent st…
Combining Model and Parameter Uncertainty in Bayesian Neural Networks
Aliaksandr Hubin, Geir Storvik
Bayesian neural networks (BNNs) have recently regained a significant amount of attention in the deep learning community due to the development of scalable approximate Bayesian infe…