no code implementations • 30 Jun 2020 • Jay S. Stanley III, Eric C. Chi, Gal Mishne
Graph signal processing (GSP) is an important methodology for studying data residing on irregular structures.
no code implementations • ICLR 2019 • Jay S. Stanley III, Guy Wolf, Smita Krishnaswamy
We leverage this assumption to estimate relations between intrinsic manifold dimensions, which are given by diffusion map coordinates over each of the datasets.
no code implementations • ICLR Workshop LLD 2019 • Daniel B. Burkhardt, Jay S. Stanley III, Ana Luisa Perdigoto, Scott A. Gigante, Kevan C. Herold, Guy Wolf, Antonio J. Giraldez, David van Dijk, Smita Krishnaswamy
Single-cell RNA-sequencing (scRNA-seq) is a powerful tool for analyzing biological systems.
no code implementations • 31 Jan 2019 • Scott Gigante, Jay S. Stanley III, Ngan Vu, David van Dijk, Kevin Moon, Guy Wolf, Smita Krishnaswamy
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.
1 code implementation • ICLR 2019 • Alexander Tong, David van Dijk, Jay S. Stanley III, Matthew Amodio, Kristina Yim, Rebecca Muhle, James Noonan, Guy Wolf, Smita Krishnaswamy
Taking inspiration from spatial organization and localization of neuron activations in biological networks, we use a graph Laplacian penalty to structure the activations within a layer.
no code implementations • 30 Sep 2018 • Jay S. Stanley III, Scott Gigante, Guy Wolf, Smita Krishnaswamy
We use this to relate the diffusion coordinates of each dataset through our assumption of partial feature correspondence.
1 code implementation • 14 Feb 2018 • Ofir Lindenbaum, Jay S. Stanley III, Guy Wolf, Smita Krishnaswamy
Then, it generates new points evenly along the manifold by pulling randomly generated points into its intrinsic structure using a diffusion kernel.
