19 citations · 24 across the 7 of their papers we have counts for
7 papers
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…
Trading Information between Latents in Hierarchical Variational Autoencoders
Tim Z. Xiao, Robert Bamler
Variational Autoencoders (VAEs) were originally motivated (Kingma & Welling, 2014) as probabilistic generative models in which one performs approximate Bayesian inference. The prop…
Iterative Teaching by Data Hallucination
Zeju Qiu, Weiyang Liu, Tim Z. Xiao +5
We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i.e., a pool of…
Out-of-Distribution Detection with Class Ratio Estimation
Mingtian Zhang, Andi Zhang, Tim Z. Xiao +2
Density-based Out-of-distribution (OOD) detection has recently been shown unreliable for the task of detecting OOD images. Various density ratio based approaches achieve good empir…
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…
Locally-Contextual Nonlinear CRFs for Sequence Labeling
Harshil Shah, Tim Xiao, David Barber
Linear chain conditional random fields (CRFs) combined with contextual word embeddings have achieved state of the art performance on sequence labeling tasks. In many of these tasks…
Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformers
Tim Z. Xiao, Aidan N. Gomez, Yarin Gal
We detect out-of-training-distribution sentences in Neural Machine Translation using the Bayesian Deep Learning equivalent of Transformer models. For this we develop a new measure…