2 citations · 3 across the 4 of their papers we have counts for
3 papers · 1 filter
SynQP: A Framework and Metrics for Evaluating the Quality and Privacy Risk of Synthetic Data
Bing Hu, Yixin Li, Asma Bahamyirou +1
The use of synthetic data in health applications raises privacy concerns, yet the lack of open frameworks for privacy evaluations has slowed its adoption. A major challenge is the…
Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching
Aaron Havens, Benjamin Kurt Miller, Bing Yan +10
We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions. It is the fi…
Synthetic Data from Diffusion Models Improve Drug Discovery Prediction
Bing Hu, Ashish Saragadam, Anita Layton +1
Artificial intelligence (AI) is increasingly used in every stage of drug development. Continuing breakthroughs in AI-based methods for drug discovery require the creation, improvem…