E-LIB Suche - Ergebnisse für: Hubin, Aliaksandr

1

Sparse Bayesian Neural Networks: Bridging Model and Paramet..:

Hubin, Aliaksandr ; Storvik, Geir
Mathematics. 12 (2024) 6 - p. 788 , 2024

Link: https://doi.org/10.3390/..

2

Fractional Polynomial Models as Special Cases of Bayesian G..:

Hubin, Aliaksandr ; Heinze, Georg ; De Bin, Riccardo
Fractal and Fractional. 7 (2023) 9 - p. 641 , 2023

Link: https://doi.org/10.3390/..

3

Prior distributions expressing ignorance about convex incre..:

Gåsemyr, Jørund ; Hubin, Aliaksandr
Scandinavian Journal of Statistics. 50 (2022) 2 - p. 452-476 , 2022

Link: https://doi.org/10.1111/..

4

A subsampling approach for Bayesian model selection:

Lachmann, Jon ; Storvik, Geir ; Frommlet, Florian.
International Journal of Approximate Reasoning. 151 (2022) - p. 33-63 , 2022

Link: https://doi.org/10.1016/..

5

An adaptive simulated annealing EM algorithm for inference ..:

, In: Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing,

Hubin, Aliaksandr - p. 1-9 , 2019

Link: https://dl.acm.org/doi/1..

6

Mode jumping MCMC for Bayesian variable selection in GLMM:

Hubin, Aliaksandr ; Storvik, Geir
Computational Statistics & Data Analysis. 127 (2018) - p. 281-297 , 2018

Link: https://doi.org/10.1016/..

7

Position Paper: Bayesian Deep Learning in the Age of Large-..:

Papamarkou, Theodore ; Skoularidou, Maria ; Palla, Konstantina...
http://arxiv.org/abs/2402.00809. , 2024

Link: http://arxiv.org/abs/240..

8

Fractional Polynomials Models as Special Cases of Bayesian ..:

Hubin, Aliaksandr ; Heinze, Georg ; De Bin, Riccardo
http://arxiv.org/abs/2305.15903. , 2023

Link: http://arxiv.org/abs/230..

9

Fractional Polynomial Models as Special Cases of Bayesian G..:

Hubin, Aliaksandr ; Heinze, Georg ; De Bin, Riccardo
Hubin, Aliaksandr Heinze, Georg De Bin, Riccardo . Fractional Polynomial Models as Special Cases of Bayesian Generalized Nonlinear Models. Fractal and Fractional. 2023, 7(9), 1-23. , 2023

Link: http://hdl.handle.net/10..

10

Subsampling MCMC for Bayesian Variable Selection and Model ..:

Lachmann, Jon ; Hubin, Aliaksandr
http://arxiv.org/abs/2312.16997. , 2023

Link: http://arxiv.org/abs/231..

11

Sparsifying Bayesian neural networks with latent binary var..:

Skaaret-Lund, Lars ; Storvik, Geir ; Hubin, Aliaksandr
http://arxiv.org/abs/2305.03395. , 2023

Link: http://arxiv.org/abs/230..

12

Variational Inference for Bayesian Neural Networks under Mo..:

Hubin, Aliaksandr ; Storvik, Geir
http://arxiv.org/abs/2305.00934. , 2023

Link: http://arxiv.org/abs/230..

13

A subsampling approach for Bayesian model selection ; ENEng..:

Lachmann, Jon ; Storvik, Geir Olve ; Frommlet, Florian.
Lachmann, Jon Storvik, Geir Olve Frommlet, Florian Hubin, Aliaksandr . A subsampling approach for Bayesian model selection. International Journal of Approximate Reasoning. 2022, 151, 33-63. , 2022

Link: http://hdl.handle.net/10..

14

skweak: Weak Supervision Made Easy for NLP:

Lison, Pierre ; Barnes, Jeremy ; Hubin, Aliaksandr
http://arxiv.org/abs/2104.09683. , 2021

Link: http://arxiv.org/abs/210..

15

Flexible Bayesian Nonlinear Model Configuration:

Hubin, Aliaksandr ; Storvik, Geir ; Frommlet, Florian
https://jair.org/index.php/jair/article/view/13047. , 2021

Link: https://hdl.handle.net/1..