15 citations · 30 across the 7 of their papers we have counts for
7 papers
Architecture Generalization with MetaNCA
Meet Barot, Daniel Berenberg, Sina Khajehabdollahi
Self-organization is an emergent property of life, driven by the collective behavior of individual components acting on local information. Biological neurons, through local interac…
OpenProteinSet: Training data for structural biology at scale
Gustaf Ahdritz, Nazim Bouatta, Sachin Kadyan +7
Multiple sequence alignments (MSAs) of proteins encode rich biological information and have been workhorses in bioinformatic methods for tasks like protein design and protein struc…
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…
Multi-segment preserving sampling for deep manifold sampler
Daniel Berenberg, Jae Hyeon Lee, Simon Kelow +6
Deep generative modeling for biological sequences presents a unique challenge in reconciling the bias-variance trade-off between explicit biological insight and model flexibility.…
Inferring the size of the causal universe: features and fusion of causal attribution networks
Daniel Berenberg, James P. Bagrow
Cause-and-effect reasoning, the attribution of effects to causes, is one of the most powerful and unique skills humans possess. Multiple surveys are mapping out causal attributions…
Efficient Crowd Exploration of Large Networks: The Case of Causal Attribution
Daniel Berenberg, James P. Bagrow
Accurately and efficiently crowdsourcing complex, open-ended tasks can be difficult, as crowd participants tend to favor short, repetitive "microtasks". We study the crowdsourcing…