51 citations · 91 across the 6 of their papers we have counts for
6 papers
Contrastive Diffusion Alignment: Learning Structured Latents for Controllable Generation
Ruchi Sandilya, Sumaira Perez, Charles Lynch +9
Diffusion models excel at generation, but their latent spaces are high dimensional and not explicitly organized for interpretation or control. We introduce ConDA (Contrastive Diffu…
Simple and Scalable Algorithms for Cluster-Aware Precision Medicine
Amanda M. Buch, Conor Liston, Logan Grosenick
AI-enabled precision medicine promises a transformational improvement in healthcare outcomes by enabling data-driven personalized diagnosis, prognosis, and treatment. However, the…
Self-tracking Energy Transfer for Neural Stimulation in Untethered Mice
John S. Ho, Yuji Tanabe, Shrivats Mohan Iyer +5
Optical or electrical stimulation of neural circuits in mice during natural behavior is an important paradigm for studying brain function. Conventional systems for optogenetics and…
The Open Connectome Project Data Cluster: Scalable Analysis and Vision for High-Throughput Neuroscience
Randal Burns, William Gray Roncal, Dean Kleissas +17
We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for t…
Whole-brain Prediction Analysis with GraphNet
Logan Grosenick, Brad Klingenberg, Kiefer Katovich +2
Multivariate machine learning methods are increasingly used to analyze neuroimaging data, often replacing more traditional "mass univariate" techniques that fit data one voxel at a…
A Generalized Least Squares Matrix Decomposition
Genevera I. Allen, Logan Grosenick, Jonathan Taylor
Variables in many massive high-dimensional data sets are structured, arising for example from measurements on a regular grid as in imaging and time series or from spatial-temporal…