6 citations · 7 across the 5 of their papers we have counts for
5 papers
Adaptive Label Error Detection: A Bayesian Approach to Mislabeled Data Detection
Zan Chaudhry, Noam H. Rotenberg, Brian Caffo +2
Machine learning classification systems are susceptible to poor performance when trained with incorrect ground truth labels, even when data is well-curated by expert annotators. As…
Evidential Uncertainty Quantification: A Variance-Based Perspective
Ruxiao Duan, Brian Caffo, Harrison X. Bai +2
Uncertainty quantification of deep neural networks has become an active field of research and plays a crucial role in various downstream tasks such as active learning. Recent advan…
Applications of Sequential Learning for Medical Image Classification
Sohaib Naim, Brian Caffo, Haris I Sair +1
Purpose: The aim of this work is to develop a neural network training framework for continual training of small amounts of medical imaging data and create heuristics to assess trai…
The Multiple Subnetwork Hypothesis: Enabling Multidomain Learning by Isolating Task-Specific Subnetworks in Feedforward Neural Networks
Jacob Renn, Ian Sotnek, Benjamin Harvey +1
Neural networks have seen an explosion of usage and research in the past decade, particularly within the domains of computer vision and natural language processing. However, only r…
Prospective Learning: Principled Extrapolation to the Future
Ashwin De Silva, Rahul Ramesh, Lyle Ungar +40
Learning is a process which can update decision rules, based on past experience, such that future performance improves. Traditionally, machine learning is often evaluated under the…