3 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
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…
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…