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Found 6 papers, 1 papers with code
Learning Active Subspaces for Effective and Scalable Uncertainty Quantification in Deep Neural Networks
Bayesian inference for neural networks, or Bayesian deep learning, has the potential to provide well-calibrated predictions with quantified uncertainty and robustness.
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.
ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation
The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators.
Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness
To this end, we propose a Unified probabilistic architecture and weight ensembling Neural Architecture Search (UraeNAS) that leverages advances in probabilistic neural architecture search and approximate Bayesian inference to generate ensembles form the joint distribution of neural network architectures and weights.
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.
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.
