25 citations · 94 across the 21 of their papers we have counts for
23 papers
Benchmarking the Connectomes of Caenorhabditis elegans within the Reservoir Computing Framework
Felix S. Reimers, Ola Huse Ramstad, Aliaksandr Hubin +1
The aim of this work is to examine the connectomes of Caenorhabditis elegans through a computational lens using the reservoir computing framework. Connectomes are mappings of biolo…
Exploring napping paradigm for Recurrent Spiking Neural Networks
Andreas Massey, Stefano Nichele, Aliaksandr Hubin
Biological organisms minimize free energy by balancing two competing demands on their internal world model: it must be accurate enough to predict sensory input, yet simple enough t…
Bayesian Fractional Polynomials for Optimal Dosage Estimation with Fish Nutrition Applications
Aliaksandr Hubin, Åshild Krogdahl, Guro Løkka +1
The problem of optimal dosage estimation arises in diverse scientific domains, from pharmacology and toxicology to aquaculture and environmental studies. Statistical modeling of no…
Sleep-Based Homeostatic Regularization for Stabilizing Spike-Timing-Dependent Plasticity in Recurrent Spiking Neural Networks
Andreas Massey, Aliaksandr Hubin, Stefano Nichele +1
Spike-timing-dependent plasticity (STDP) provides a biologically-plausible learning mechanism for spiking neural networks (SNNs); however, Hebbian weight updates in architectures w…
FBMS: An R Package for Flexible Bayesian Model Selection and Model Averaging
Florian Frommlet, Jon Lachmann, Geir Storvik +1
The FBMS R package facilitates Bayesian model selection and model averaging in complex regression settings by employing a variety of Monte Carlo model exploration methods. At its c…
Bayesian Generalized Nonlinear Models Offer Basis Free SINDy With Model Uncertainty
Aliaksandr Hubin
Sparse Identification of Nonlinear Dynamics (SINDy) has become a standard methodology for inferring governing equations of dynamical systems from observed data using statistical mo…
Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks
Eirik Høyheim, Lars Skaaret-Lund, Solve Sæbø +1
Modeling natural phenomena with artificial neural networks (ANNs) often provides highly accurate predictions. However, ANNs often suffer from over-parameterization, complicating in…
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla +22
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language dat…
Subsampling MCMC for Bayesian Variable Selection and Model Averaging in BGNLM
Jon Lachmann, Aliaksandr Hubin
Bayesian Generalized Nonlinear Models (BGNLM) offer a flexible nonlinear alternative to GLM while still providing better interpretability than machine learning techniques such as n…
Fractional Polynomials Models as Special Cases of Bayesian Generalized Nonlinear Models
Aliaksandr Hubin, Georg Heinze, Riccardo De Bin
We propose a framework for fitting fractional polynomials models as special cases of Bayesian Generalized Nonlinear Models, applying an adapted version of the Genetically Modified…
Sparsifying Bayesian neural networks with latent binary variables and normalizing flows
Lars Skaaret-Lund, Geir Storvik, Aliaksandr Hubin
Artificial neural networks (ANNs) are powerful machine learning methods used in many modern applications such as facial recognition, machine translation, and cancer diagnostics. A…
Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty
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…
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…
Flexible Bayesian Nonlinear Model Configuration
Aliaksandr Hubin, Geir Storvik, Florian Frommlet
Regression models are used in a wide range of applications providing a powerful scientific tool for researchers from different fields. Linear, or simple parametric, models are ofte…
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…
Deep Bayesian regression models
Aliaksandr Hubin, Geir Storvik, Florian Frommlet
Regression models are used for inference and prediction in a wide range of applications providing a powerful scientific tool for researchers and analysts from different fields. In…
A novel algorithmic approach to Bayesian Logic Regression
Aliaksandr Hubin, Geir Storvik, Florian Frommlet
Logic regression was developed more than a decade ago as a tool to construct predictors from Boolean combinations of binary covariates. It has been mainly used to model epistatic e…
Estimating the marginal likelihood with Integrated nested Laplace approximation (INLA)
Aliaksandr Hubin, Geir Storvik
The marginal likelihood is a well established model selection criterion in Bayesian statistics. It also allows to efficiently calculate the marginal posterior model probabilities t…
Mode jumping MCMC for Bayesian variable selection in GLMM
Aliaksandr Hubin, Geir Storvik
Generalized linear mixed models (GLMM) are used for inference and prediction in a wide range of different applications providing a powerful scientific tool. An increasing number of…