Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Hubin, Aliaksandr"'
Autor:
Papamarkou, Theodore, Skoularidou, Maria, Palla, Konstantina, Aitchison, Laurence, Arbel, Julyan, Dunson, David, Filippone, Maurizio, Fortuin, Vincent, Hennig, Philipp, Hernández-Lobato, José Miguel, Hubin, Aliaksandr, Immer, Alexander, Karaletsos, Theofanis, Khan, Mohammad Emtiyaz, Kristiadi, Agustinus, Li, Yingzhen, Mandt, Stephan, Nemeth, Christopher, Osborne, Michael A., Rudner, Tim G. J., Rügamer, David, Teh, Yee Whye, Welling, Max, Wilson, Andrew Gordon, Zhang, Ruqi
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 datasets. However, a broader perspective reveals a multitude of overlooke
Externí odkaz:
http://arxiv.org/abs/2402.00809
Autor:
Lachmann, Jon, Hubin, Aliaksandr
Bayesian Generalized Nonlinear Models (BGNLM) offer a flexible nonlinear alternative to GLM while still providing better interpretability than machine learning techniques such as neural networks. In BGNLM, the methods of Bayesian Variable Selection a
Externí odkaz:
http://arxiv.org/abs/2312.16997
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 Mode Jumping Markov Chain Monte Carlo algorithm. The universality of t
Externí odkaz:
http://arxiv.org/abs/2305.15903
Artificial neural networks (ANNs) are powerful machine learning methods used in many modern applications such as facial recognition, machine translation, and cancer diagnostics. A common issue with ANNs is that they usually have millions or billions
Externí odkaz:
http://arxiv.org/abs/2305.03395
Autor:
Hubin, Aliaksandr, Storvik, Geir
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 inference techniques. There are several advantages of using a Bayesian app
Externí odkaz:
http://arxiv.org/abs/2305.00934
Autor:
Digranes, Nora, Hoeberg, Emma, Lervik, Andreas, Hubin, Aliaksandr, Nordgreen, Janicke, Haga, Henning A.
Publikováno v:
In Veterinary Anaesthesia and Analgesia September-October 2024 51(5):491-499
Publikováno v:
Published in Proceedings of 22nd European Young Statisticians Meeting (ISBN: 978-960-7943-23-1), 2021. URL: https://www.eysm2021.panteion.gr/files/Proceedings_EYSM_2021.pdf Parpoula & Athanasios Rakitzis
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 in complex model spaces, where the number of explanatory variables is
Externí odkaz:
http://arxiv.org/abs/2110.05316
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 machine learning paradigm based on a simple idea: instead of labelling data
Externí odkaz:
http://arxiv.org/abs/2104.09683
Autor:
Hubin, Aliaksandr1,2,3,4 (AUTHOR) aliaksah@math.uio.no, Storvik, Geir2,4 (AUTHOR)
Publikováno v:
Mathematics (2227-7390). Mar2024, Vol. 12 Issue 6, p788. 28p.
Publikováno v:
Bayesian Analysis, Volume 15, Number 1 (2020)
In this rejoinder we summarize the comments, questions and remarks on the paper "A novel algorithmic approach to Bayesian Logic Regression" from the discussants. We then respond to those comments, questions and remarks, provide several extensions of
Externí odkaz:
http://arxiv.org/abs/2005.00605