Search Results for author:
Found 5 papers, 2 papers with code
OpenProteinSet: Training data for structural biology at scale
Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein structure prediction for decades.
Protein Discovery with Discrete Walk-Jump Sampling
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 Markov chain Monte Carlo (MCMC), and projecting back to the true data manifold with one-step denoising.
Neural language representations predict outcomes of scientific research
Many research fields codify their findings in standard formats, often by reporting correlations between quantities of interest.
Inferring the size of the causal universe: features and fusion of causal attribution networks
Further, the total size of the collective causal attribution network held by humans is currently unknown, making it challenging to assess the progress of these surveys.
Multi-segment preserving sampling for deep manifold sampler
We introduce an alternative approach to this guided sampling procedure, multi-segment preserving sampling, that enables the direct inclusion of domain-specific knowledge by designating preserved and non-preserved segments along the input sequence, thereby restricting variation to only select regions.
