6 papers
Measure-to-measure Regression with Transformers
Matthew Vandergrift, Martha White, Yury Polyanskiy +2
Many learning problems require predicting how populations evolve under an unknown transformation. A natural representation for such populations is a probability measure, with point…
A Call to Lagrangian Action: Learning Population Mechanics from Temporal Snapshots
Vincent Guan, Lazar Atanackovic, Kirill Neklyudov
The population dynamics of molecules, cells, and organisms are governed by a number of unknown forces. In the last decade, population dynamics have predominantly been modeled with…
Curly Flow Matching for Learning Non-gradient Field Dynamics
Katarina PetroviÄ, Lazar Atanackovic, Viggo Moro +5
Modeling the transport dynamics of natural processes from population-level observations is a ubiquitous problem in the natural sciences. Such models rely on key assumptions about t…
Simulation-free Structure Learning for Stochastic Dynamics
Noah El Rimawi-Fine, Adam Stecklov, Lucas Nelson +4
Modeling dynamical systems and unraveling their underlying causal relationships is central to many domains in the natural sciences. Various physical systems, such as those arising…
Meta Flow Matching: Integrating Vector Fields on the Wasserstein Manifold
Lazar Atanackovic, Xi Zhang, Brandon Amos +5
Numerous biological and physical processes can be modeled as systems of interacting entities evolving continuously over time, e.g. the dynamics of communicating cells or physical p…
The Superposition of Diffusion Models Using the Itô Density Estimator
Marta Skreta, Lazar Atanackovic, Avishek Joey Bose +2
The Cambrian explosion of easily accessible pre-trained diffusion models suggests a demand for methods that combine multiple different pre-trained diffusion models without incurrin…