17 citations · 18 across the 4 of their papers we have counts for
5 papers · 1 filter
SystemMatch: optimizing preclinical drug models to human clinical outcomes via generative latent-space matching
Scott Gigante, Varsha G. Raghavan, Amanda M. Robinson +7
Translating the relevance of preclinical models (, animal models, or organoids) to their relevance in humans presents an important challenge during drug developm…
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…
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…
Harmonic Alignment
Jay S. Stanley, Scott Gigante, Guy Wolf +1
We propose a novel framework for combining datasets via alignment of their intrinsic geometry. This alignment can be used to fuse data originating from disparate modalities, or to…
Modeling Global Dynamics from Local Snapshots with Deep Generative Neural Networks
Scott Gigante, David van Dijk, Kevin Moon +3
Complex high dimensional stochastic dynamic systems arise in many applications in the natural sciences and especially biology. However, while these systems are difficult to describ…