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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...
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04/17/2023
Leveraging sparse and shared feature activations for disentangled representation learning
Recovering the latent factors of variation of high dimensional data has ...
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03/04/2023
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
Deep ensembles (DE) have been successful in improving model performance ...
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12/15/2022
Are Multimodal Models Robust to Image and Text Perturbations?
Multimodal image-text models have shown remarkable performance in the pa...
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07/19/2022
Assaying Out-Of-Distribution Generalization in Transfer Learning
Since out-of-distribution generalization is a generally ill-posed proble...
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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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06/20/2021
On Stein Variational Neural Network Ensembles
Ensembles of deep neural networks have achieved great success recently, ...
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06/07/2021
Uncertainty Baselines: Benchmarks for Uncertainty Robustness in Deep Learning
High-quality estimates of uncertainty and robustness are crucial for num...
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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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06/24/2020
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Ensembles over neural network weights trained from different random init...
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02/26/2020
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
We propose automated augmented conjugate inference, a new inference meth...
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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...
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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...
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07/04/2018
Quasi-Monte Carlo Variational Inference
Many machine learning problems involve Monte Carlo gradient estimators. ...
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03/21/2018
Scalable Generalized Dynamic Topic Models
Dynamic topic models (DTMs) model the evolution of prevalent themes in l...
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02/18/2018
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
We propose an efficient stochastic variational approach to GP classifica...
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07/18/2017