Florian Wenzel | DeepAI

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∙ 04/20/2023

A data augmentation perspective on diffusion models and retrieval

Diffusion models excel at generating photorealistic images from text-que...

0 Max F. Burg, et al. ∙

∙ 04/17/2023

Leveraging sparse and shared feature activations for disentangled representation learning

Recovering the latent factors of variation of high dimensional data has ...

0 Marco Fumero, et al. ∙

∙ 03/04/2023

Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries

Deep ensembles (DE) have been successful in improving model performance ...

0 Charlotte Loh, et al. ∙

∙ 12/15/2022

Are Multimodal Models Robust to Image and Text Perturbations?

Multimodal image-text models have shown remarkable performance in the pa...

10 Jielin Qiu, et al. ∙

∙ 07/19/2022

Assaying Out-Of-Distribution Generalization in Transfer Learning

Since out-of-distribution generalization is a generally ill-posed proble...

6 Florian Wenzel, et al. ∙

∙ 10/07/2021

Sparse MoEs meet Efficient Ensembles

Machine learning models based on the aggregated outputs of submodels, ei...

3 James Urquhart Allingham, et al. ∙

∙ 10/06/2021

Deep Classifiers with Label Noise Modeling and Distance Awareness

Uncertainty estimation in deep learning has recently emerged as a crucia...

0 Vincent Fortuin, et al. ∙

∙ 06/20/2021

On Stein Variational Neural Network Ensembles

Ensembles of deep neural networks have achieved great success recently, ...

0 Francesco D'Angelo, et al. ∙

∙ 06/07/2021

Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning

High-quality estimates of uncertainty and robustness are crucial for num...

0 Zachary Nado, et al. ∙

∙ 02/12/2021

Bayesian Neural Network Priors Revisited

Isotropic Gaussian priors are the de facto standard for modern Bayesian ...

16 Vincent Fortuin, et al. ∙

∙ 06/24/2020

Hyperparameter Ensembles for Robustness and Uncertainty Quantification

Ensembles over neural network weights trained from different random init...

13 Florian Wenzel, et al. ∙

∙ 02/26/2020

Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models

We propose automated augmented conjugate inference, a new inference meth...

0 Theo Galy-Fajou, et al. ∙

∙ 02/06/2020

How Good is the Bayes Posterior in Deep Neural Networks Really?

During the past five years the Bayesian deep learning community has deve...

27 Florian Wenzel, et al. ∙

∙ 05/23/2019

Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation

We propose a new scalable multi-class Gaussian process classification ap...

6 Theo Galy-Fajou, et al. ∙

∙ 07/04/2018

Quasi-Monte Carlo Variational Inference

Many machine learning problems involve Monte Carlo gradient estimators. ...

0 Alexander Buchholz, et al. ∙

∙ 03/21/2018

Scalable Generalized Dynamic Topic Models

Dynamic topic models (DTMs) model the evolution of prevalent themes in l...

0 Patrick Jähnichen, et al. ∙

∙ 02/18/2018

Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation

We propose an efficient stochastic variational approach to GP classifica...

0 Florian Wenzel, et al. ∙

∙ 07/18/2017

Bayesian Nonlinear Support Vector Machines for Big Data

We propose a fast inference method for Bayesian nonlinear support vector...

0 Florian Wenzel, et al. ∙