10 citations · 21 across the 14 of their papers we have counts for
14 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…
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…
Moment Matching Denoising Gibbs Sampling
Mingtian Zhang, Alex Hawkins-Hooker, Brooks Paige +1
Energy-Based Models (EBMs) offer a versatile framework for modeling complex data distributions. However, training and sampling from EBMs continue to pose significant challenges. Th…
Towards Healing the Blindness of Score Matching
Mingtian Zhang, Oscar Key, Peter Hayes +3
Score-based divergences have been widely used in machine learning and statistics applications. Despite their empirical success, a blindness problem has been observed when using the…
Integrated Weak Learning
Peter Hayes, Mingtian Zhang, Raza Habib +3
We introduce Integrated Weak Learning, a principled framework that integrates weak supervision into the training process of machine learning models. Our approach jointly trains the…
Improving VAE-based Representation Learning
Mingtian Zhang, Tim Z. Xiao, Brooks Paige +1
Latent variable models like the Variational Auto-Encoder (VAE) are commonly used to learn representations of images. However, for downstream tasks like semantic classification, the…
Generalization Gap in Amortized Inference
Mingtian Zhang, Peter Hayes, David Barber
The ability of likelihood-based probabilistic models to generalize to unseen data is central to many machine learning applications such as lossless compression. In this work, we st…
Parallel Neural Local Lossless Compression
Mingtian Zhang, James Townsend, Ning Kang +1
The recently proposed Neural Local Lossless Compression (NeLLoC), which is based on a local autoregressive model, has achieved state-of-the-art (SOTA) out-of-distribution (OOD) gen…
Variational f-divergence Minimization
Mingtian Zhang, Thomas Bird, Raza Habib +2
Probabilistic models are often trained by maximum likelihood, which corresponds to minimizing a specific f-divergence between the model and data distribution. In light of recent su…
Spread Divergence
Mingtian Zhang, Peter Hayes, Tom Bird +2
For distributions and with different supports or undefined densities, the divergence may not exist. We define a Sprea…