5 papers
Let Physics Guide Your Protein Flows: Topology-aware Unfolding and Generation
Yogesh Verma, Markus Heinonen, Vikas Garg
Protein structure prediction and folding are fundamental to understanding biology, with recent deep learning advances reshaping the field. Diffusion-based generative models have re…
Positional Encoding meets Persistent Homology on Graphs
Yogesh Verma, Amauri H. Souza, Vikas Garg
The local inductive bias of message-passing graph neural networks (GNNs) hampers their ability to exploit key structural information (e.g., connectivity and cycles). Positional enc…
Robust Simulation-Based Inference under Missing Data via Neural Processes
Yogesh Verma, Ayush Bharti, Vikas Garg
Simulation-based inference (SBI) methods typically require fully observed data to infer parameters of models with intractable likelihood functions. However, datasets often contain…
Devil is in the Details: Density Guidance for Detail-Aware Generation with Flow Models
Rafał Karczewski, Markus Heinonen, Vikas Garg
Diffusion models have emerged as a powerful class of generative models, capable of producing high-quality images by mapping noise to a data distribution. However, recent findings s…
Diffusion Models as Cartoonists: The Curious Case of High Density Regions
Rafał Karczewski, Markus Heinonen, Vikas Garg
We investigate what kind of images lie in the high-density regions of diffusion models. We introduce a theoretical mode-tracking process capable of pinpointing the exact mode of th…