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09/14/2023
Hodge-Aware Contrastive Learning
Simplicial complexes prove effective in modeling data with multiway depe...
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06/29/2023
Understanding Pathologies of Deep Heteroskedastic Regression
Several recent studies have reported negative results when using heteros...
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05/26/2023
Laplace-Approximated Neural Additive Models: Improving Interpretability with Bayesian Inference
Deep neural networks (DNNs) have found successful applications in many f...
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04/17/2023
Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
The linearized-Laplace approximation (LLA) has been shown to be effectiv...
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04/04/2023
Incorporating Unlabelled Data into Bayesian Neural Networks
We develop a contrastive framework for learning better prior distributio...
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11/14/2022
PAC-Bayesian Meta-Learning: From Theory to Practice
Meta-Learning aims to accelerate the learning on new tasks by acquiring ...
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02/22/2022
Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations
Data augmentation is commonly applied to improve performance of deep lea...
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10/15/2021
Probing as Quantifying the Inductive Bias of Pre-trained Representations
Pre-trained contextual representations have led to dramatic performance ...
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10/08/2021
Pathologies in priors and inference for Bayesian transformers
In recent years, the transformer has established itself as a workhorse i...
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10/07/2021
Sparse MoEs meet Efficient Ensembles
Machine learning models based on the aggregated outputs of submodels, ei...
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10/06/2021
Deep Classifiers with Label Noise Modeling and Distance Awareness
Uncertainty estimation in deep learning has recently emerged as a crucia...
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07/22/2021
Neural Variational Gradient Descent
Particle-based approximate Bayesian inference approaches such as Stein V...
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07/20/2021
A Bayesian Approach to Invariant Deep Neural Networks
We propose a novel Bayesian neural network architecture that can learn i...
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06/22/2021
Repulsive Deep Ensembles are Bayesian
Deep ensembles have recently gained popularity in the deep learning comm...
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06/20/2021
On Stein Variational Neural Network Ensembles
Ensembles of deep neural networks have achieved great success recently, ...
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06/10/2021
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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05/14/2021
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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05/14/2021
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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04/11/2021
Scalable Marginal Likelihood Estimation for Model Selection in Deep Learning
Marginal-likelihood based model-selection, even though promising, is rar...
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02/12/2021
Bayesian Neural Network Priors Revisited
Isotropic Gaussian priors are the de facto standard for modern Bayesian ...
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02/10/2021
On Disentanglement in Gaussian Process Variational Autoencoders
Complex multivariate time series arise in many fields, ranging from comp...
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02/02/2021
Exact Langevin Dynamics with Stochastic Gradients
Stochastic gradient Markov Chain Monte Carlo algorithms are popular samp...
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01/24/2021
Annealed Stein Variational Gradient Descent
Particle based optimization algorithms have recently been developed as s...
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11/14/2020
Factorized Gaussian Process Variational Autoencoders
Variational autoencoders often assume isotropic Gaussian priors and mean...
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10/26/2020
Scalable Gaussian Process Variational Autoencoders
Conventional variational autoencoders fail in modeling correlations betw...
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10/20/2020
Sparse Gaussian Process Variational Autoencoders
Large, multi-dimensional spatio-temporal datasets are omnipresent in mod...
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02/13/2020
PACOH: Bayes-Optimal Meta-Learning with PAC-Guarantees
Meta-learning can successfully acquire useful inductive biases from data...
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10/17/2019
Mixture-of-Experts Variational Autoencoder for clustering and generating from similarity-based representations
Clustering high-dimensional data, such as images or biological measureme...
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10/03/2019
Variational PSOM: Deep Probabilistic Clustering with Self-Organizing Maps
Generating visualizations and interpretations from high-dimensional data...
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09/28/2019
Deep Multiple Instance Learning for Taxonomic Classification of Metagenomic read sets
Metagenomic studies have increasingly utilized sequencing technologies i...
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09/27/2019
MGP-AttTCN: 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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07/09/2019
Multivariate Time Series Imputation with Variational Autoencoders
Multivariate time series with missing values are common in many areas, f...
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01/23/2019
Deep Mean Functions for Meta-Learning in Gaussian Processes
Fitting machine learning models in the low-data limit is challenging. Th...
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10/24/2018
Scalable Gaussian Processes on Discrete Domains
Kernel methods on discrete domains have shown great promise for many cha...
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06/06/2018