8 papers
Image-to-image Mapping with Many Domains by Sparse Attribute Transfer
Matthew Amodio, Rim Assouel, Victor Schmidt +3
Unsupervised image-to-image translation consists of learning a pair of mappings between two domains without known pairwise correspondences between points. The current convention is…
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
Alexander Tong, Jessie Huang, Guy Wolf +2
It is increasingly common to encounter data from dynamic processes captured by static cross-sectional measurements over time, particularly in biomedical settings. Recent attempts t…
Making Logic Learnable With Neural Networks
Tobias Brudermueller, Dennis L. Shung, Adrian J. Stanley +2
While neural networks are good at learning unspecified functions from training samples, they cannot be directly implemented in hardware and are often not interpretable or formally…
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…
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…