3 citations · 5 across the 25 of their papers we have counts for
7 papers · 1 filter
Optimizing Decoding Paths in Masked Diffusion Models by Quantifying Uncertainty
Ziyu Chen, Xinbei Jiang, Peng Sun +1
Masked Diffusion Models (MDMs) offer flexible, non-autoregressive generation, but this freedom introduces a challenge: final output quality is highly sensitive to the decoding orde…
Bootstrap Dynamic-Aware 3D Visual Representation for Scalable Robot Learning
Qiwei Liang, Boyang Cai, Minghao Lai +6
Despite strong results on recognition and segmentation, current 3D visual pre-training methods often underperform on robotic manipulation. We attribute this gap to two factors: the…
Fast and Stable Diffusion Planning through Variational Adaptive Weighting
Zhiying Qiu, Tao Lin
Diffusion models have recently shown promise in offline RL. However, these methods often suffer from high training costs and slow convergence, particularly when using transformer-b…
Unified Continuous Generative Models
Peng Sun, Yi Jiang, Tao Lin
Recent advances in continuous generative models, including multi-step approaches like diffusion and flow-matching (typically requiring 8-1000 sampling steps) and few-step methods s…
Equally Critical: Samples, Targets, and Their Mappings in Datasets
Runkang Yang, Peng Sun, Xinyi Shang +2
Data inherently possesses dual attributes: samples and targets. For targets, knowledge distillation has been widely employed to accelerate model convergence, primarily relying on t…
Collaborative Unlabeled Data Optimization
Xinyi Shang, Peng Sun, Fengyuan Liu +1
This paper pioneers a novel data-centric paradigm to maximize the utility of unlabeled data, tackling a critical question: How can we enhance the efficiency and sustainability of d…