828 citations · 5k across the 89 of their papers we have counts for
100 papers
Sample, estimate, aggregate: A recipe for causal discovery foundation models
Menghua Wu, Yujia Bao, Regina Barzilay +1
Causal discovery, the task of inferring causal structure from data, has the potential to uncover mechanistic insights from biological experiments, especially those involving pertur…
Predictive Chemistry Augmented with Text Retrieval
Yujie Qian, Zhening Li, Zhengkai Tu +2
This paper focuses on using natural language descriptions to enhance predictive models in the chemistry field. Conventionally, chemoinformatics models are trained with extensive st…
Fast non-autoregressive inverse folding with discrete diffusion
John J. Yang, Jason Yim, Regina Barzilay +1
Generating protein sequences that fold into a intended 3D structure is a fundamental step in de novo protein design. De facto methods utilize autoregressive generation, but this es…
Risk-Controlling Model Selection via Guided Bayesian Optimization
Bracha Laufer-Goldshtein, Adam Fisch, Regina Barzilay +1
Adjustable hyperparameters of machine learning models typically impact various key trade-offs such as accuracy, fairness, robustness, or inference cost. Our goal in this paper is t…
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…
Fast protein backbone generation with SE(3) flow matching
Jason Yim, Andrew Campbell, Andrew Y. K. Foong +8
We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching. Specifically, we adapt FrameDiff, a state-of-the-art diffusion model, to the flow-mat…