2 citations · 3 across the 3 of their papers we have counts for
7 papers
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…
Domain-Agnostic Clustering with Self-Distillation
Mohammed Adnan, Yani A. Ioannou, Chuan-Yung Tsai +1
Recent advancements in self-supervised learning have reduced the gap between supervised and unsupervised representation learning. However, most self-supervised and deep clustering…
Rapid Classification of TESS Planet Candidates with Convolutional Neural Networks
Hugh P. Osborn, Megan Ansdell, Yani Ioannou +6
Accurately and rapidly classifying exoplanet candidates from transit surveys is a goal of growing importance as the data rates from space-based survey missions increases. This is e…
Scientific Domain Knowledge Improves Exoplanet Transit Classification with Deep Learning
Megan Ansdell, Yani Ioannou, Hugh P. Osborn +5
Space-based missions such as Kepler, and soon TESS, provide large datasets that must be analyzed efficiently and systematically. Recent work by Shallue & Vanderburg (2018) successf…
Refining Architectures of Deep Convolutional Neural Networks
Sukrit Shankar, Duncan Robertson, Yani Ioannou +2
Deep Convolutional Neural Networks (CNNs) have recently evinced immense success for various image recognition tasks. However, a question of paramount importance is somewhat unanswe…