2 citations · 3 across the 8 of their papers we have counts for
9 papers
Simulation-Free Learning of Population Dynamics with Wasserstein Lagrangian Residuals
Fedor Sergeev, Markus Heinonen, Daniel Waxman +4
The dynamics of cells, organisms, and fluids are often modeled as probability distributions evolving over time. Reconstructing and extrapolating this evolution from unpaired snapsh…
Wasserstein Residuals: Learning Gradient Flows from Population Dynamics
Markus Heinonen, Yair Shenfeld, Ricardo Baptista +4
Reconstructing population dynamics is a central problem in the physical and data sciences. Often, the dynamics are modeled as a Wasserstein gradient flow (WGF): a curve of distribu…
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…
The Spacetime of Diffusion Models: An Information Geometry Perspective
Rafał Karczewski, Markus Heinonen, Alison Pouplin +2
We present a novel geometric perspective on the latent space of diffusion models. We first show that the standard pullback approach, utilizing the deterministic probability flow OD…
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…
What Ails Generative Structure-based Drug Design: Expressivity is Too Little or Too Much?
Rafał Karczewski, Samuel Kaski, Markus Heinonen +1
Several generative models with elaborate training and sampling procedures have been proposed to accelerate structure-based drug design (SBDD); however, their empirical performance…
ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs
Yogesh Verma, Markus Heinonen, Vikas Garg
Climate and weather prediction traditionally relies on complex numerical simulations of atmospheric physics. Deep learning approaches, such as transformers, have recently challenge…
E(3)-equivariant models cannot learn chirality: Field-based molecular generation
Alexandru Dumitrescu, Dani Korpela, Markus Heinonen +4
Obtaining the desired effect of drugs is highly dependent on their molecular geometries. Thus, the current prevailing paradigm focuses on 3D point-cloud atom representations, utili…