7 citations · 16 across the 9 of their papers we have counts for
9 papers
A mechanistically interpretable neural network for regulatory genomics
Alex M. Tseng, Gokcen Eraslan, Tommaso Biancalani +1
Deep neural networks excel in mapping genomic DNA sequences to associated readouts (e.g., protein-DNA binding). Beyond prediction, the goal of these networks is to reveal to scient…
Fine-Tuning of Continuous-Time Diffusion Models as Entropy-Regularized Control
Masatoshi Uehara, Yulai Zhao, Kevin Black +6
Diffusion models excel at capturing complex data distributions, such as those of natural images and proteins. While diffusion models are trained to represent the distribution in th…
Feedback Efficient Online Fine-Tuning of Diffusion Models
Masatoshi Uehara, Yulai Zhao, Kevin Black +6
Diffusion models excel at modeling complex data distributions, including those of images, proteins, and small molecules. However, in many cases, our goal is to model parts of the d…
Complex Preferences for Different Convergent Priors in Discrete Graph Diffusion
Alex M. Tseng, Nathaniel Diamant, Tommaso Biancalani +1
Diffusion models have achieved state-of-the-art performance in generating many different kinds of data, including images, text, and videos. Despite their success, there has been li…
Accurate and Efficient Structural Ensemble Generation of Macrocyclic Peptides using Internal Coordinate Diffusion
Colin A. Grambow, Hayley Weir, Nathaniel L. Diamant +3
Macrocyclic peptides are an emerging therapeutic modality, yet computational approaches for accurately sampling their diverse 3D ensembles remain challenging due to their conformat…
GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion
Alex M. Tseng, Nathaniel Diamant, Tommaso Biancalani +1
Diffusion models achieve state-of-the-art performance in generating realistic objects and have been successfully applied to images, text, and videos. Recent work has shown that dif…
Improving Graph Generation by Restricting Graph Bandwidth
Nathaniel Diamant, Alex M. Tseng, Kangway V. Chuang +2
Deep graph generative modeling has proven capable of learning the distribution of complex, multi-scale structures characterizing real-world graphs. However, one of the main limitat…
Hierarchically branched diffusion models leverage dataset structure for class-conditional generation
Alex M. Tseng, Max Shen, Tommaso Biancalani +1
Class-labeled datasets, particularly those common in scientific domains, are rife with internal structure, yet current class-conditional diffusion models ignore these relationships…
Conditional Diffusion with Less Explicit Guidance via Model Predictive Control
Max W. Shen, Ehsan Hajiramezanali, Gabriele Scalia +4
How much explicit guidance is necessary for conditional diffusion? We consider the problem of conditional sampling using an unconditional diffusion model and limited explicit guida…