3 citations · 14 across the 18 of their papers we have counts for
7 papers · 1 filter
Entangled Schrödinger Bridge Matching
Sophia Tang, Yinuo Zhang, Pranam Chatterjee
Simulating trajectories of multi-particle systems on complex energy landscapes is a central task in molecular dynamics (MD) and drug discovery, but remains challenging at scale due…
AReUReDi: Annealed Rectified Updates for Refining Discrete Flows with Multi-Objective Guidance
Tong Chen, Yinuo Zhang, Pranam Chatterjee
Designing sequences that satisfy multiple, often conflicting, objectives is a central challenge in therapeutic and biomolecular engineering. Existing generative frameworks largely…
TR2-D2: Tree Search Guided Trajectory-Aware Fine-Tuning for Discrete Diffusion
Sophia Tang, Yuchen Zhu, Molei Tao +1
Reinforcement learning with stochastic optimal control offers a promising framework for diffusion fine-tuning, where a pre-trained diffusion model is optimized to generate paths th…
Branched Schrödinger Bridge Matching
Sophia Tang, Yinuo Zhang, Alexander Tong +1
Predicting the intermediate trajectories between an initial and target distribution is a central problem in generative modeling. Existing approaches, such as flow matching and Schr…
Multi-Objective-Guided Discrete Flow Matching for Controllable Biological Sequence Design
Tong Chen, Yinuo Zhang, Sophia Tang +1
Designing biological sequences that satisfy multiple, often conflicting, functional and biophysical criteria remains a central challenge in biomolecule engineering. While discrete…
Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation
Sophia Tang, Yinuo Zhang, Alexander Tong +1
Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and pr…
Path Planning for Masked Diffusion Model Sampling
Fred Zhangzhi Peng, Zachary Bezemek, Sawan Patel +5
Any order generation of discrete data using masked diffusion models (MDMs) offers a compelling alternative to traditional autoregressive models, especially in domains that lack a n…