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Found 10 papers, 3 papers with code
A comprehensive study of spike and slab shrinkage priors for structurally sparse Bayesian neural networks
In this paper, we explore two well-established shrinkage techniques, Lasso and Horseshoe, for model compression in Bayesian neural networks.
Error Controlled Feature Selection for Ultrahigh Dimensional and Highly Correlated Feature Space Using Deep Learning
In recent years, deep learning has been at the center of analytics due to its impressive empirical success in analyzing complex data objects.
Sequential Bayesian Neural Subnetwork Ensembles
Deep neural network ensembles that appeal to model diversity have been used successfully to improve predictive performance and model robustness in several applications.
Sparsity-Inducing Categorical Prior Improves Robustness of the Information Bottleneck
In addition, it provides a mechanism for learning a joint distribution of the latent variable and the sparsity and hence can account for the complete uncertainty in the latent space.
Coupled Support Tensor Machine Classification for Multimodal Neuroimaging Data
Learning from multimodal data is of great interest in machine learning and statistics research as this offers the possibility of capturing complementary information among modalities.
Layer Adaptive Node Selection in Bayesian Neural Networks: Statistical Guarantees and Implementation Details
Although several works have studied theoretical and numerical properties of sparse neural architectures, they have primarily focused on the edge selection.
TEC: Tensor Ensemble Classifier for Big Data
Besides, we highlight the trade-off between the computational cost and the prediction risk for TEC model.
Variational Bayes Neural Network: Posterior Consistency, Classification Accuracy and Computational Challenges
This paper develops a variational Bayesian neural network estimation methodology and related statistical theory.
Statistical Foundation of Variational Bayes Neural Networks
However there are few results which revolve around the theoretical properties of VB, especially in non-parametric problems.
Variational Inference with Vine Copulas: An efficient Approach for Bayesian Computer Model Calibration
With the advancements of computer architectures, the use of computational models proliferates to solve complex problems in many scientific applications such as nuclear physics and climate research.
