3 citations · 6 across the 5 of their papers we have counts for
5 papers
How Data Augmentation Shapes Neural Representations
Tianxiao He, Alex H. Williams, Sarah E. Harvey
Data augmentation is widely recognized for improving generalization in deep networks, yet its impact on the geometry of learned representations remains poorly understood. In this w…
What Representational Similarity Measures Imply about Decodable Information
Sarah E. Harvey, David Lipshutz, Alex H. Williams
Neural responses encode information that is useful for a variety of downstream tasks. A common approach to understand these systems is to build regression models or ``decoders'' th…
Discriminating image representations with principal distortions
Jenelle Feather, David Lipshutz, Sarah E. Harvey +2
Image representations (artificial or biological) are often compared in terms of their global geometric structure; however, representations with similar global structure can have st…
Duality of Bures and Shape Distances with Implications for Comparing Neural Representations
Sarah E. Harvey, Brett W. Larsen, Alex H. Williams
A multitude of (dis)similarity measures between neural network representations have been proposed, resulting in a fragmented research landscape. Most of these measures fall into on…
Estimating Shape Distances on Neural Representations with Limited Samples
Dean A. Pospisil, Brett W. Larsen, Sarah E. Harvey +1
Measuring geometric similarity between high-dimensional network representations is a topic of longstanding interest to neuroscience and deep learning. Although many methods have be…