From the 1 of 10 linked papers with an AI index.
10 papers
Mirror Learning
Yunpeng Liu, Matthew Niedoba, Oluwanifemi A. Adekanye +4
We investigate imitation learning through the lens of third-person observation and propose a framework for mirror learning: acquiring actionable policies from passive observation.…
Integration Matters: Rollout-Based Training for Constrained Diffusion Models
Xiaoxuan Liang, Saeid Naderiparizi, Berend Zwartsenberg +1
The paper introduces a fine‑tuning method that uses online rollout to guide constraint enforcement during training of diffusion models, aligning training with the sampling process…
Filtered Posterior Mean Collections: A Unified Framework for Analytical Models of Diffusion Generalization
Matthew Niedoba, Berend Zwartsenberg, Frank Wood
The neural-network denoising functions which form the backbone of image diffusion models are remarkably consistent in their generalization behaviour across a wide variety of networ…
Improved Constrained Generation by Bridging Pretrained Generative Models
Xiaoxuan Liang, Saeid Naderiparizi, Yunpeng Liu +2
Constrained generative modeling is fundamental to applications such as robotic control and autonomous driving, where models must respect physical laws and safety-critical constrain…
Lifelong Learning of Video Diffusion Models From a Single Video Stream
Jason Yoo, Yingchen He, Saeid Naderiparizi +4
This work demonstrates that training autoregressive video diffusion models from a single video stream$\unicode{x2013}$resembling the experience of embodied agents$\unicode{x2013}$i…
Towards a Mechanistic Explanation of Diffusion Model Generalization
Matthew Niedoba, Berend Zwartsenberg, Kevin Murphy +1
We propose a simple, training-free mechanism which explains the generalization behaviour of diffusion models. By comparing pre-trained diffusion models to their theoretically optim…