Search Results for author:
Found 22 papers, 12 papers with code
On the Challenges and Opportunities in Generative AI
The field of deep generative modeling has grown rapidly and consistently over the years.
Evaluating the Fairness of Discriminative Foundation Models in Computer Vision
We propose a novel taxonomy for bias evaluation of discriminative foundation models, such as Contrastive Language-Pretraining (CLIP), that are used for labeling tasks.
Image retrieval outperforms diffusion models on data augmentation
Many approaches have been proposed to use diffusion models to augment training datasets for downstream tasks, such as classification.
Leveraging sparse and shared feature activations for disentangled representation learning
Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings.
Multi-Symmetry Ensembles: Improving Diversity and Generalization via Opposing Symmetries
In this work, we present Multi-Symmetry Ensembles (MSE), a framework for constructing diverse ensembles by capturing the multiplicity of hypotheses along symmetry axes, which explore the hypothesis space beyond stochastic perturbations of model weights and hyperparameters.
Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift
Multimodal image-text models have shown remarkable performance in the past few years.
Assaying Out-Of-Distribution Generalization in Transfer Learning
Since out-of-distribution generalization is a generally ill-posed problem, various proxy targets (e. g., calibration, adversarial robustness, algorithmic corruptions, invariance across shifts) were studied across different research programs resulting in different recommendations.
Adversarial Robustness
Out-of-Distribution Generalization
+1
Sparse MoEs meet Efficient Ensembles
Machine learning models based on the aggregated outputs of submodels, either at the activation or prediction levels, often exhibit strong performance compared to individual models.
Deep Classifiers with Label Noise Modeling and Distance Awareness
Uncertainty estimation in deep learning has recently emerged as a crucial area of interest to advance reliability and robustness in safety-critical applications.
On Stein Variational Neural Network Ensembles
Ensembles of deep neural networks have achieved great success recently, but they do not offer a proper Bayesian justification.
Uncertainty Baselines: Benchmarks for Uncertainty & Robustness in Deep Learning
In this paper we introduce Uncertainty Baselines: high-quality implementations of standard and state-of-the-art deep learning methods on a variety of tasks.
Bayesian Neural Network Priors Revisited
Isotropic Gaussian priors are the de facto standard for modern Bayesian neural network inference.
Distilling Ensembles Improves Uncertainty Estimates
We seek to bridge the performance gap between batch ensembles (ensembles of deep networks with shared parameters) and deep ensembles on tasks which require not only predictions, but also uncertainty estimates for these predictions.
Hyperparameter Ensembles for Robustness and Uncertainty Quantification
Ensembles over neural network weights trained from different random initialization, known as deep ensembles, achieve state-of-the-art accuracy and calibration.
Automated Augmented Conjugate Inference for Non-conjugate Gaussian Process Models
Building on the conjugate structure of the augmented model, we develop two inference methods.
How Good is the Bayes Posterior in Deep Neural Networks Really?
In this work we cast doubt on the current understanding of Bayes posteriors in popular deep neural networks: we demonstrate through careful MCMC sampling that the posterior predictive induced by the Bayes posterior yields systematically worse predictions compared to simpler methods including point estimates obtained from SGD.
Multi-Class Gaussian Process Classification Made Conjugate: Efficient Inference via Data Augmentation
We propose a new scalable multi-class Gaussian process classification approach building on a novel modified softmax likelihood function.
Quasi-Monte Carlo Variational Inference
We also propose a new algorithm for Monte Carlo objectives, where we operate with a constant learning rate and increase the number of QMC samples per iteration.
Scalable Generalized Dynamic Topic Models
First, we extend the class of tractable priors from Wiener processes to the generic class of Gaussian processes (GPs).
Efficient Gaussian Process Classification Using Polya-Gamma Data Augmentation
We propose a scalable stochastic variational approach to GP classification building on Polya-Gamma data augmentation and inducing points.
Bayesian Nonlinear Support Vector Machines for Big Data
We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points.
Sparse Probit Linear Mixed Model
Formulated as models for linear regression, LMMs have been restricted to continuous phenotypes.
