1 citations · 1 across the 9 of their papers we have counts for
9 papers
Normal Guidance is what Attention Needs
Ethan Harvey, Dennis Johan Loevlie, Michael C. Hughes
We consider training classifiers for 3D medical images using only one binary label for the entire volume rather than a label for each 2D slice. In such weakly supervised settings,…
A Multi-Dataset Benchmark of Multiple Instance Learning for 3D Neuroimage Classification
Ethan Harvey, Dennis Johan Loevlie, Amir Ali Satani +3
Despite being resource-intensive to train, 3D convolutional neural networks (CNNs) have been the standard approach to classify CT and MRI scans. Recent work suggests that deep mult…
Occam's Razor is Only as Sharp as Your ELBO
Ethan Harvey, Michael C. Hughes, Geoff Pleiss
The marginal likelihood, also known as the evidence, is regarded as a mathematical embodiment of Occam's razor, enabling hyperparameter selection that avoids overfitting. The evide…
Synthetic Data Reveals Generalization Gaps in Correlated Multiple Instance Learning
Ethan Harvey, Dennis Johan Loevlie, Michael C. Hughes
Multiple instance learning (MIL) is often used in medical imaging to classify high-resolution 2D images by processing patches or classify 3D volumes by processing slices. However,…
Learning Hyperparameters via a Data-Emphasized Variational Objective
Ethan Harvey, Mikhail Petrov, Michael C. Hughes
When training large models on limited data, avoiding overfitting is paramount. Common grid search or smarter search methods rely on expensive separate runs for each candidate hyper…
Learning the Regularization Strength for Deep Fine-Tuning via a Data-Emphasized Variational Objective
Ethan Harvey, Mikhail Petrov, Michael C. Hughes
A number of popular transfer learning methods rely on grid search to select regularization hyperparameters that control over-fitting. This grid search requirement has several key d…
Transfer Learning with Informative Priors: Simple Baselines Better than Previously Reported
Ethan Harvey, Mikhail Petrov, Michael C. Hughes
We pursue transfer learning to improve classifier accuracy on a target task with few labeled examples available for training. Recent work suggests that using a source task to learn…
A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data
Ethan Harvey, Wansu Chen, David M. Kent +1
Practitioners building classifiers often start with a smaller pilot dataset and plan to grow to larger data in the near future. Such projects need a toolkit for extrapolating how m…
A Comparative Analysis of Machine Learning Models for Early Detection of Hospital-Acquired Infections
Ethan Harvey, Junzi Dong, Erina Ghosh +1
As more and more infection-specific machine learning models are developed and planned for clinical deployment, simultaneously running predictions from different models may provide…