
Pathologies in priors and inference for Bayesian transformers
In recent years, the transformer has established itself as a workhorse i...
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Sparse MoEs meet Efficient Ensembles
Machine learning models based on the aggregated outputs of submodels, ei...
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Deep Classifiers with Label Noise Modeling and Distance Awareness
Uncertainty estimation in deep learning has recently emerged as a crucia...
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Neural Variational Gradient Descent
Particlebased approximate Bayesian inference approaches such as Stein V...
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A Bayesian Approach to Invariant Deep Neural Networks
We propose a novel Bayesian neural network architecture that can learn i...
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Repulsive Deep Ensembles are Bayesian
Deep ensembles have recently gained popularity in the deep learning comm...
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On Stein Variational Neural Network Ensembles
Ensembles of deep neural networks have achieved great success recently, ...
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Data augmentation in Bayesian neural networks and the cold posterior effect
Data augmentation is a highly effective approach for improving performan...
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BNNpriors: A library for Bayesian neural network inference with different prior distributions
Bayesian neural networks have shown great promise in many applications w...
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Priors in Bayesian Deep Learning: A Review
While the choice of prior is one of the most critical parts of the Bayes...
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Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Marginallikelihood based modelselection, even though promising, is rar...
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Bayesian Neural Network Priors Revisited
Isotropic Gaussian priors are the de facto standard for modern Bayesian ...
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On Disentanglement in Gaussian Process Variational Autoencoders
Complex multivariate time series arise in many fields, ranging from comp...
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Exact Langevin Dynamics with Stochastic Gradients
Stochastic gradient Markov Chain Monte Carlo algorithms are popular samp...
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Annealed Stein Variational Gradient Descent
Particle based optimization algorithms have recently been developed as s...
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Factorized Gaussian Process Variational Autoencoders
Variational autoencoders often assume isotropic Gaussian priors and mean...
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Scalable Gaussian Process Variational Autoencoders
Conventional variational autoencoders fail in modeling correlations betw...
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Sparse Gaussian Process Variational Autoencoders
Large, multidimensional spatiotemporal datasets are omnipresent in mod...
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PACOH: BayesOptimal MetaLearning with PACGuarantees
Metalearning can successfully acquire useful inductive biases from data...
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MixtureofExperts Variational Autoencoder for clustering and generating from similaritybased representations
Clustering highdimensional data, such as images or biological measureme...
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Variational PSOM: Deep Probabilistic Clustering with SelfOrganizing Maps
Generating visualizations and interpretations from highdimensional data...
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Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets
Metagenomic studies have increasingly utilized sequencing technologies i...
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MGPAttTCN: An Interpretable Machine Learning Model for the Prediction of Sepsis
With a mortality rate of 5.4 million lives worldwide every year and a he...
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Multivariate Time Series Imputation with Variational Autoencoders
Multivariate time series with missing values are common in many areas, f...
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Deep Mean Functions for MetaLearning in Gaussian Processes
Fitting machine learning models in the lowdata limit is challenging. Th...
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Scalable Gaussian Processes on Discrete Domains
Kernel methods on discrete domains have shown great promise for many cha...
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Deep SelfOrganization: Interpretable Discrete Representation Learning on Time Series
Human professionals are often required to make decisions based on comple...
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Vincent Fortuin
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