9 papers
Constraint-Aware Flow Matching: Decision Aligned End-to-End Training for Constrained Sampling
Jacob K. Christopher, James E. Warner, Ferdinando Fioretto
Deep generative models provide state-of-the-art performance across a wide array of applications, with recent studies showing increasing applicability for science and engineering. D…
Constrained Diffusion for Protein Design with Hard Structural Constraints
Jacob K. Christopher, Austin Seamann, Jingyi Cui +2
Diffusion models offer a powerful means of capturing the manifold of realistic protein structures, enabling rapid design for protein engineering tasks. However, existing approaches…
Constrained Discrete Diffusion
Michael Cardei, Jacob K Christopher, Thomas Hartvigsen +2
Discrete diffusion models are a class of generative models that construct sequences by progressively denoising samples from a categorical noise distribution. Beyond their rapidly g…
SpecDiff-2: Scaling Diffusion Drafter Alignment For Faster Speculative Decoding
Jameson Sandler, Jacob K. Christopher, Thomas Hartvigsen +1
Speculative decoding has become the standard approach for accelerating Large Language Model (LLM) inference. It exploits a lossless draft-then-verify procedure to circumvent the la…
Training-Free Constrained Generation With Stable Diffusion Models
Stefano Zampini, Jacob K. Christopher, Luca Oneto +2
Stable diffusion models represent the state-of-the-art in data synthesis across diverse domains and hold transformative potential for applications in science and engineering, e.g.,…
Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models
Jinhao Liang, Jacob K Christopher, Sven Koenig +1
Recent advances in diffusion models hold significant potential in robotics, enabling the generation of diverse and smooth trajectories directly from raw representations of the envi…