1 citations · 1 across the 7 of their papers we have counts for
7 papers
VarDiU: A Variational Diffusive Upper Bound for One-Step Diffusion Distillation
Leyang Wang, Mingtian Zhang, Zijing Ou +1
Recently, diffusion distillation methods have compressed thousand-step teacher diffusion models into one-step student generators while preserving sample quality. Most existing appr…
DiffRatio: Training One-Step Diffusion Models Without Teacher Supervision
Wenlin Chen, Mingtian Zhang, Jiajun He +4
Score-based distillation methods (e.g., variational score distillation) train one-step diffusion models by first pre-training a teacher score model and then distilling it into a on…
Training Neural Samplers with Reverse Diffusive KL Divergence
Jiajun He, Wenlin Chen, Mingtian Zhang +2
Training generative models to sample from unnormalized density functions is an important and challenging task in machine learning. Traditional training methods often rely on the re…
Improving Probabilistic Diffusion Models With Optimal Diagonal Covariance Matching
Zijing Ou, Mingtian Zhang, Andi Zhang +3
The probabilistic diffusion model has become highly effective across various domains. Typically, sampling from a diffusion model involves using a denoising distribution characteriz…
Mafin: Enhancing Black-Box Embeddings with Model Augmented Fine-Tuning
Mingtian Zhang, Shawn Lan, Peter Hayes +1
Retrieval Augmented Generation (RAG) has emerged as an effective solution for mitigating hallucinations in Large Language Models (LLMs). The retrieval stage in RAG typically involv…
Active Preference Learning for Large Language Models
William Muldrew, Peter Hayes, Mingtian Zhang +1
As large language models (LLMs) become more capable, fine-tuning techniques for aligning with human intent are increasingly important. A key consideration for aligning these models…
Diffusive Gibbs Sampling
Wenlin Chen, Mingtian Zhang, Brooks Paige +2
The inadequate mixing of conventional Markov Chain Monte Carlo (MCMC) methods for multi-modal distributions presents a significant challenge in practical applications such as Bayes…