44 citations · 253 across the 39 of their papers we have counts for
6 papers · 1 filter
Visualizing the PHATE of Neural Networks
Scott Gigante, Adam S. Charles, Smita Krishnaswamy +1
Understanding why and how certain neural networks outperform others is key to guiding future development of network architectures and optimization methods. To this end, we introduc…
Coarse Graining of Data via Inhomogeneous Diffusion Condensation
Nathan Brugnone, Alex Gonopolskiy, Mark W. Moyle +7
Big data often has emergent structure that exists at multiple levels of abstraction, which are useful for characterizing complex interactions and dynamics of the observations. Here…
Fixing Bias in Reconstruction-based Anomaly Detection with Lipschitz Discriminators
Alexander Tong, Guy Wolf, Smita Krishnaswamy
Anomaly detection is of great interest in fields where abnormalities need to be identified and corrected (e.g., medicine and finance). Deep learning methods for this task often rel…
TraVeLGAN: Image-to-image Translation by Transformation Vector Learning
Matthew Amodio, Smita Krishnaswamy
Interest in image-to-image translation has grown substantially in recent years with the success of unsupervised models based on the cycle-consistency assumption. The achievements o…
Compressed Diffusion
Scott Gigante, Jay S. Stanley, Ngan Vu +4
Diffusion maps are a commonly used kernel-based method for manifold learning, which can reveal intrinsic structures in data and embed them in low dimensions. However, as with most…
Generating and Aligning from Data Geometries with Generative Adversarial Networks
Matthew Amodio, Smita Krishnaswamy
Unsupervised domain mapping has attracted substantial attention in recent years due to the success of models based on the cycle-consistency assumption. These models map between two…