3 papers
cs.LG2026
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…
cs.CV2025
Exploring Magnitude Preservation and Rotation Modulation in Diffusion Transformers
Eric Tillman Bill, Cristian Perez Jensen, Sotiris Anagnostidis +1
Denoising diffusion models exhibit remarkable generative capabilities, but remain challenging to train due to their inherent stochasticity, where high-variance gradient estimates l…
cs.LG2025
Efficient Distillation of Classifier-Free Guidance using Adapters
Cristian Perez Jensen, Seyedmorteza Sadat
While classifier-free guidance (CFG) is essential for conditional diffusion models, it doubles the number of neural function evaluations (NFEs) per inference step. To mitigate this…