3.6k citations · 5.7k across the 34 of their papers we have counts for
4 papers · 1 filter
Estimating Performance for Safe Deployment of Machine Learning Models
Zeju Li, Konstantinos Kamnitsas, Mobarak I. Hoque +5
The main challenge in applying deep neural networks to real-world scenarios lies in distributional discrepancies between training and test datasets, often caused by variations in d…
Federated Learning Enables Big Data for Rare Cancer Boundary Detection
Sarthak Pati, Ujjwal Baid, Brandon Edwards +276
Although machine learning (ML) has shown promise in numerous domains, there are concerns about generalizability to out-of-sample data. This is currently addressed by centrally shar…
Overfitting of neural nets under class imbalance: Analysis and improvements for segmentation
Zeju Li, Konstantinos Kamnitsas, Ben Glocker
Overfitting in deep learning has been the focus of a number of recent works, yet its exact impact on the behavior of neural networks is not well understood. This study analyzes ove…
Semi-Supervised Learning via Compact Latent Space Clustering
Konstantinos Kamnitsas, Daniel C. Castro, Loic Le Folgoc +6
We present a novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation. The key idea is to…