2 citations · 3 across the 4 of their papers we have counts for
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What's the score? Automated Denoising Score Matching for Nonlinear Diffusions
Raghav Singhal, Mark Goldstein, Rajesh Ranganath
Reversing a diffusion process by learning its score forms the heart of diffusion-based generative modeling and for estimating properties of scientific systems. The diffusion proces…
Adaptive Sampling of k-Space in Magnetic Resonance for Rapid Pathology Prediction
Chen-Yu Yen, Raghav Singhal, Umang Sharma +3
Magnetic Resonance (MR) imaging, despite its proven diagnostic utility, remains an inaccessible imaging modality for disease surveillance at the population level. A major factor re…
Where to Diffuse, How to Diffuse, and How to Get Back: Automated Learning for Multivariate Diffusions
Raghav Singhal, Mark Goldstein, Rajesh Ranganath
Diffusion-based generative models (DBGMs) perturb data to a target noise distribution and reverse this process to generate samples. The choice of noising process, or inference diff…
On the Feasibility of Machine Learning Augmented Magnetic Resonance for Point-of-Care Identification of Disease
Raghav Singhal, Mukund Sudarshan, Anish Mahishi +7
Early detection of many life-threatening diseases (e.g., prostate and breast cancer) within at-risk population can improve clinical outcomes and reduce cost of care. While numerous…