2.7k citations · 4k across the 68 of their papers we have counts for
6 papers · 1 filter
Bounding generalization error with input compression: An empirical study with infinite-width networks
Angus Galloway, Anna Golubeva, Mahmoud Salem +3
Estimating the Generalization Error (GE) of Deep Neural Networks (DNNs) is an important task that often relies on availability of held-out data. The ability to better predict GE ba…
Monitoring Shortcut Learning using Mutual Information
Mohammed Adnan, Yani Ioannou, Chuan-Yung Tsai +3
The failure of deep neural networks to generalize to out-of-distribution data is a well-known problem and raises concerns about the deployment of trained networks in safety-critica…
Understanding the impact of image and input resolution on deep digital pathology patch classifiers
Eu Wern Teh, Graham W. Taylor
We consider annotation efficient learning in Digital Pathology (DP), where expert annotations are expensive and thus scarce. We explore the impact of image and input resolution on…
DeepRNG: Towards Deep Reinforcement Learning-Assisted Generative Testing of Software
Chuan-Yung Tsai, Graham W. Taylor
Although machine learning (ML) has been successful in automating various software engineering needs, software testing still remains a highly challenging topic. In this paper, we ai…
Learning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images
Eu Wern Teh, Graham W. Taylor
A critical challenge of training deep learning models in the Digital Pathology (DP) domain is the high annotation cost by medical experts. One way to tackle this issue is via trans…
On Evaluation Metrics for Graph Generative Models
Rylee Thompson, Boris Knyazev, Elahe Ghalebi +2
In image generation, generative models can be evaluated naturally by visually inspecting model outputs. However, this is not always the case for graph generative models (GGMs), mak…