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Found 5 papers, 4 papers with code
Improving robustness to corruptions with multiplicative weight perturbations
Deep neural networks (DNNs) excel on clean images but struggle with corrupted ones.
Input-gradient space particle inference for neural network ensembles
To sidestep these difficulties, we propose First-order Repulsive Deep Ensemble (FoRDE), an ensemble learning method based on ParVI, which performs repulsion in the space of first-order input gradients.
Tackling covariate shift with node-based Bayesian neural networks
In this paper, we interpret these latent noise variables as implicit representations of simple and domain-agnostic data perturbations during training, producing BNNs that perform well under covariate shift due to input corruptions.
Scalable Bayesian neural networks by layer-wise input augmentation
We introduce implicit Bayesian neural networks, a simple and scalable approach for uncertainty representation in deep learning.
Nested Variational Autoencoder for Topic Modeling on Microtexts with Word Vectors
The objective of our research is to create a topic model that can achieve great performances on microtexts while requiring a small runtime for scalability to large datasets.
