51 citations · 104 across the 35 of their papers we have counts for
20 papers · 1 filter
Breaking the Factorization Barrier in Diffusion Language Models
Ian Li, Zilei Shao, Benjie Wang +3
Diffusion language models theoretically allow for efficient parallel generation but are practically hindered by the ``factorization barrier'': the assumption that simultaneously pr…
Lookahead Path Likelihood Optimization for Diffusion LLMs
Xuejie Liu, Yap Vit Chun, Yitao Liang +1
Diffusion Large Language Models (dLLMs) support arbitrary-order generation, yet their inference performance critically depends on the unmasking order. Existing strategies rely on h…
Training One Model to Master Cross-Level Agentic Actions via Reinforcement Learning
Kaichen He, Zihao Wang, Muyao Li +2
The paradigm of agentic AI is shifting from engineered complex workflows to post-training native models. However, existing agents are typically confined to static, predefined actio…
Zero-Variance Gradients for Variational Autoencoders
Zilei Shao, Anji Liu, Guy Van den Broeck
Training deep generative models like Variational Autoencoders (VAEs) requires propagating gradients through stochastic latent variables, which introduces estimation variance that c…
Rethinking Probabilistic Circuit Parameter Learning
Anji Liu, Zilei Shao, Guy Van den Broeck
Probabilistic Circuits (PCs) offer a computationally scalable framework for generative modeling, supporting exact and efficient inference of a wide range of probabilistic queries.…
Rao-Blackwell Gradient Estimators for Equivariant Denoising Diffusion
Vinh Tong, Hoang Trung-Dung, Anji Liu +2
In domains such as molecular and protein generation, physical systems exhibit inherent symmetries that are critical to model. Two main strategies have emerged for learning invarian…