274 citations · 389 across the 18 of their papers we have counts for
18 papers
Protein Discovery with Discrete Walk-Jump Sampling
Nathan C. Frey, Daniel Berenberg, Karina Zadorozhny +10
We resolve difficulties in training and sampling from a discrete generative model by learning a smoothed energy function, sampling from the smoothed data manifold with Langevin Mar…
3D molecule generation by denoising voxel grids
Pedro O. Pinheiro, Joshua Rackers, Joseph Kleinhenz +6
We propose a new score-based approach to generate 3D molecules represented as atomic densities on regular grids. First, we train a denoising neural network that learns to map from…
Chain of Log-Concave Markov Chains
Saeed Saremi, Ji Won Park, Francis Bach
We introduce a theoretical framework for sampling from unnormalized densities based on a smoothing scheme that uses an isotropic Gaussian kernel with a single fixed noise scale. We…
Universal Smoothed Score Functions for Generative Modeling
Saeed Saremi, Rupesh Kumar Srivastava, Francis Bach
We consider the problem of generative modeling based on smoothing an unknown density of interest in using factorial kernels with independent Gaussian channels wi…
PropertyDAG: Multi-objective Bayesian optimization of partially ordered, mixed-variable properties for biological sequence design
Ji Won Park, Samuel Stanton, Saeed Saremi +6
Bayesian optimization offers a sample-efficient framework for navigating the exploration-exploitation trade-off in the vast design space of biological sequences. Whereas it is poss…
Multimeasurement Generative Models
Saeed Saremi, Rupesh Kumar Srivastava
We formally map the problem of sampling from an unknown distribution with a density in to the problem of learning and sampling a smoother density in $\mathbb{R}^{Md}…