39 citations · 39 across the 5 of their papers we have counts for
5 papers
Learning over Positive and Negative Edges with Contrastive Message Passing
Peter Pao-Huang, Charilaos I. Kanatsoulis, Michael Bereket +1
Conventional approaches to learning on graphs involve message passing along existing (i.e., positive) edges to update node features. However, these approaches often disregard the p…
Generative Modeling with Flux Matching
Peter Pao-Huang, Xiaojie Qiu, Stefano Ermon
We introduce Flux Matching, a new paradigm for generative modeling that generalizes existing score-based models to a broader family of vector fields that need not be conservative.…
Geometric Generative Modeling with Noise-Conditioned Graph Networks
Peter Pao-Huang, Mitchell Black, Xiaojie Qiu
Generative modeling of graphs with spatial structure is essential across many applications from computer graphics to spatial genomics. Recent flow-based generative models have achi…
Generalized Open-World Semi-Supervised Object Detection
Garvita Allabadi, Ana Lucic, Siddarth Aananth +3
Traditional semi-supervised object detection methods assume a fixed set of object classes (in-distribution or ID classes) during training and deployment, which limits performance i…
EigenFold: Generative Protein Structure Prediction with Diffusion Models
Bowen Jing, Ezra Erives, Peter Pao-Huang +3
Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensem…