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Found 13 papers, 10 papers with code
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
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.
Sparsifying Bayesian neural networks with latent binary variables and normalizing flows
In this paper, we will consider two extensions to the LBBNN method: Firstly, by using the local reparametrization trick (LRT) to sample the hidden units directly, we get a more computationally efficient algorithm.
Variational Inference for Bayesian Neural Networks under Model and Parameter Uncertainty
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.
Reversible Genetically Modified Mode Jumping MCMC
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 prohibitively large for classical Markov Chain Monte Carlo methods.
skweak: Weak Supervision Made Easy for NLP
skweak is especially designed to facilitate the use of weak supervision for NLP tasks such as text classification and sequence labelling.
Rejoinder for the discussion of the paper "A novel algorithmic approach to Bayesian Logic Regression"
In this rejoinder we summarize the comments, questions and remarks on the paper "A novel algorithmic approach to Bayesian Logic Regression" from the discussants.
Named Entity Recognition without Labelled Data: A Weak Supervision Approach
When in-domain labelled data is available, transfer learning techniques can be used to adapt existing NER models to the target domain.
Flexible Bayesian Nonlinear Model Configuration
In this paper, we introduce a flexible approach for the construction and selection of highly flexible nonlinear parametric regression models.
An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov models
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 states and mean parameter of the probability distribution of the responses (for a given state) depend on the set of $p$ covariates.
Combining Model and Parameter Uncertainty in Bayesian Neural Networks
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.
Deep Bayesian regression models
DBRM can easily be extended to include latent Gaussian variables to model complex correlation structures between observations, which seems to be not easily possible with existing deep learning approaches.
Methodology 62-02, 62-09, 62F07, 62F15, 62J12, 62J05, 62J99, 62M05, 05A16, 60J22, 92D20, 90C27, 90C59 G.1.2; G.1.6; G.2.1; G.3; I.2.0; I.2.6; I.2.8; I.5.1; I.6; I.6.4
A novel algorithmic approach to Bayesian Logic Regression
Logic regression was developed more than a decade ago as a tool to construct predictors from Boolean combinations of binary covariates.
Computation 62-02, 62-09, 62F07, 62F15, 62J12, 62J05, 62J99, 62M05, 05A16, 60J22, 92D20, 90C27, 90C59
Mode jumping MCMC for Bayesian variable selection in GLMM
An increasing number of sources of data are becoming available, introducing a variety of candidate explanatory variables for these models.
Computation 62-02, 62-09, 62F07, 62F15, 62J12, 62J05, 62J99, 62M05, 05A16, 60J22, 92D20, 90C27, 90C59
