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Found 12 papers, 3 papers with code
Mobile V-MoEs: Scaling Down Vision Transformers via Sparse Mixture-of-Experts
We empirically show that our sparse Mobile Vision MoEs (V-MoEs) can achieve a better trade-off between performance and efficiency than the corresponding dense ViTs.
Adapting the Linearised Laplace Model Evidence for Modern Deep Learning
The linearised Laplace method for estimating model uncertainty has received renewed attention in the Bayesian deep learning community.
Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior
We show that for neural networks (NN) with normalisation layers, i. e. batch norm, layer norm, or group norm, the Laplace model evidence does not approximate the volume of a posterior mode and is thus unsuitable for model selection.
Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning
Deep neural networks are prone to overconfident predictions on outliers.
Laplace Redux -- Effortless Bayesian Deep Learning
Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection.
Laplace Redux - Effortless Bayesian Deep Learning
Bayesian formulations of deep learning have been shown to have compelling theoretical properties and offer practical functional benefits, such as improved predictive uncertainty quantification and model selection.
Bayesian Deep Learning via Subnetwork Inference
In particular, we implement subnetwork linearized Laplace as a simple, scalable Bayesian deep learning method: We first obtain a MAP estimate of all weights and then infer a full-covariance Gaussian posterior over a subnetwork using the linearized Laplace approximation.
Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference
In particular, we develop a practical and scalable Bayesian deep learning method that first trains a point estimate, and then infers a full covariance Gaussian posterior approximation over a subnetwork.
Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining
We introduce an improved method for efficient black-box optimization, which performs the optimization in the low-dimensional, continuous latent manifold learned by a deep generative model.
Ranked #1 on
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Bayesian Variational Autoencoders for Unsupervised Out-of-Distribution Detection
Despite their successes, deep neural networks may make unreliable predictions when faced with test data drawn from a distribution different to that of the training data, constituting a major problem for AI safety.
Mixed-Variable Bayesian Optimization
However, few methods exist for mixed-variable domains and none of them can handle discrete constraints that arise in many real-world applications.
Embedding Models for Episodic Knowledge Graphs
In this paper, we extend models for static knowledge graphs to temporal knowledge graphs.
