2 citations · 2 across the 12 of their papers we have counts for
11 papers · 1 filter
Expanding Flow Maps
Sophia Tang, Pranam Chatterjee
Flow-based generative models have enabled remarkable progress in fast and controllable generation across continuous and discrete state spaces, yet existing parameterizations are co…
A2D2: Fine-Tuning Any-Length Discrete Diffusion for Adaptive Decoding
Sophia Tang, Yuchen Zhu, Molei Tao +1
Discrete diffusion models offer a simple and stable likelihood-based framework for sequence generation, recently extended to any-length settings via token insertion. Principled rew…
Active Flow Expansion for Out-of-Distribution Discovery: from Theory to Molecules
Riccardo De Santi, Bruce Lee, Cristian Perez Jensen +6
Standard flow and diffusion pre-training matches the distribution of available data (e.g., molecules), which often covers only a small fraction of the valid design space. In genera…
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…
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…
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…