Search Results for author:
Found 7 papers, 4 papers with code
Feynman-Kac Correctors in Diffusion: Annealing, Guidance, and Product of Experts
While score-based generative models are the model of choice across diverse domains, there are limited tools available for controlling inference-time behavior in a principled manner, e. g. for composing multiple pretrained models.
Symmetry-Aware Generative Modeling through Learned Canonicalization
Generative modeling of symmetric densities has a range of applications in AI for science, from drug discovery to physics simulations.
Sampling from Energy-based Policies using Diffusion
Energy-based policies offer a flexible framework for modeling complex, multimodal behaviors in reinforcement learning (RL).
Sequence-Augmented SE(3)-Flow Matching For Conditional Protein Backbone Generation
Proteins are essential for almost all biological processes and derive their diverse functions from complex 3D structures, which are in turn determined by their amino acid sequences.
Iterated Denoising Energy Matching for Sampling from Boltzmann Densities
Efficiently generating statistically independent samples from an unnormalized probability distribution, such as equilibrium samples of many-body systems, is a foundational problem in science.
Lie Point Symmetry and Physics Informed Networks
Symmetries have been leveraged to improve the generalization of neural networks through different mechanisms from data augmentation to equivariant architectures.
SE(3)-Stochastic Flow Matching for Protein Backbone Generation
The computational design of novel protein structures has the potential to impact numerous scientific disciplines greatly.
