Publications (17)
Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models
Gabriele Corso, Yilun Xu, Valentin de Bortoli +2
In light of the widespread success of generative models, a significant amount of research has gone into speeding up their sampling time. However, generative models are often sample…
Torsional Diffusion for Molecular Conformer Generation
Bowen Jing, Gabriele Corso, Jeffrey Chang +2
Molecular conformer generation is a fundamental task in computational chemistry. Several machine learning approaches have been developed, but none have outperformed state-of-the-ar…
Modeling Molecular Structures with Intrinsic Diffusion Models
Gabriele Corso
Since its foundations, more than one hundred years ago, the field of structural biology has strived to understand and analyze the properties of molecules and their interactions by…
Learning Graph Search Heuristics
Michal Pándy, Weikang Qiu, Gabriele Corso +4
Searching for a path between two nodes in a graph is one of the most well-studied and fundamental problems in computer science. In numerous domains such as robotics, AI, or biology…
DisCo-Diff: Enhancing Continuous Diffusion Models with Discrete Latents
Yilun Xu, Gabriele Corso, Tommi Jaakkola +2
Diffusion models (DMs) have revolutionized generative learning. They utilize a diffusion process to encode data into a simple Gaussian distribution. However, encoding a complex, po…
Dirichlet Flow Matching with Applications to DNA Sequence Design
Hannes Stark, Bowen Jing, Chenyu Wang +4
Discrete diffusion or flow models could enable faster and more controllable sequence generation than autoregressive models. We show that naïve linear flow matching on the simplex…