3 citations · 10 across the 10 of their papers we have counts for
11 papers
Preserving Angles Improves Feature Distillation
Evelyn J. Mannix, Liam Hodgkinson, Howard Bondell
Knowledge distillation methods compress models by training a student network using the classification outputs of a high quality teacher model, but can fail to effectively transfer…
ComFe: An Interpretable Head for Vision Transformers
Evelyn J. Mannix, Liam Hodgkinson, Howard Bondell
Interpretable computer vision models explain their classifications through comparing the distances between the local embeddings of an image and a set of prototypes that represent t…
A Mixture of Exemplars Approach for Efficient Out-of-Distribution Detection with Foundation Models
Evelyn Mannix, Howard Bondell
One of the early weaknesses identified in deep neural networks trained for image classification tasks was their inability to provide low confidence predictions on out-of-distributi…
Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning
Evelyn J. Mannix, Howard D. Bondell
In many machine learning applications, labeling datasets can be an arduous and time-consuming task. Although research has shown that semi-supervised learning techniques can achieve…
The R2D2 Prior for Generalized Linear Mixed Models
Eric Yanchenko, Howard D. Bondell, Brian J. Reich
In Bayesian analysis, the selection of a prior distribution is typically done by considering each parameter in the model. While this can be convenient, in many scenarios it may be…
Non-stationary Gaussian process discriminant analysis with variable selection for high-dimensional functional data
W Yu, S Wade, H D Bondell +1
High-dimensional classification and feature selection tasks are ubiquitous with the recent advancement in data acquisition technology. In several application areas such as biology,…
Nonparametric Conditional Density Estimation In A Deep Learning Framework For Short-Term Forecasting
David B. Huberman, Brian J. Reich, Howard D. Bondell
Short-term forecasting is an important tool in understanding environmental processes. In this paper, we incorporate machine learning algorithms into a conditional distribution esti…
Deep Distribution Regression
Rui Li, Howard D. Bondell, Brian J. Reich
Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting. However, most…
Bayesian Regression Using a Prior on the Model Fit: The R2-D2 Shrinkage Prior
Yan Dora Zhang, Brian P. Naughton, Howard D. Bondell +1
Prior distributions for high-dimensional linear regression require specifying a joint distribution for the unobserved regression coefficients, which is inherently difficult. We ins…
Variable selection via penalized credible regions with Dirichlet-Laplace global-local shrinkage priors
Yan Zhang, Howard D. Bondell
The method of Bayesian variable selection via penalized credible regions separates model fitting and variable selection. The idea is to search for the sparsest solution within the…
A nonparametric Bayesian test of dependence
Yimin Kao, Brian J Reich, Howard D Bondell
In this article, we propose a new method for the fundamental task of testing for dependence between two groups of variables. The response densities under the null hypothesis of ind…