34 citations · 69 across the 16 of their papers we have counts for
7 papers · 1 filter
FigBO: A Generalized Acquisition Function Framework with Look-Ahead Capability for Bayesian Optimization
Hui Chen, Xuhui Fan, Zhangkai Wu +1
Bayesian optimization is a powerful technique for optimizing expensive-to-evaluate black-box functions, consisting of two main components: a surrogate model and an acquisition func…
A Survey on Pre-Trained Diffusion Model Distillations
Xuhui Fan, Zhangkai Wu, Hongyu Wu
Diffusion Models~(DMs) have emerged as the dominant approach in Generative Artificial Intelligence (GenAI), owing to their remarkable performance in tasks such as text-to-image syn…
ParamReL: Learning Parameter Space Representation via Progressively Encoding Bayesian Flow Networks
Zhangkai Wu, Xuhui Fan, Jin Li +3
The recently proposed Bayesian Flow Networks~(BFNs) show great potential in modeling parameter spaces, offering a unified strategy for handling continuous, discretized, and discret…
FedSI: Federated Subnetwork Inference for Efficient Uncertainty Quantification
Hui Chen, Hengyu Liu, Zhangkai Wu +2
While deep neural networks (DNNs) based personalized federated learning (PFL) is demanding for addressing data heterogeneity and shows promising performance, existing methods for f…
A Temporally Disentangled Contrastive Diffusion Model for Spatiotemporal Imputation
Yakun Chen, Kaize Shi, Zhangkai Wu +5
Spatiotemporal data analysis is pivotal across various domains, such as transportation, meteorology, and healthcare. The data collected in real-world scenarios are often incomplete…
Weakly Augmented Variational Autoencoder in Time Series Anomaly Detection
Zhangkai Wu, Longbing Cao, Qi Zhang +2
Due to their unsupervised training and uncertainty estimation, deep Variational Autoencoders (VAEs) have become powerful tools for reconstruction-based Time Series Anomaly Detectio…
CVAE: Gaussian Copula-based VAE Differing Disentangled from Coupled Representations with Contrastive Posterior
Zhangkai Wu, Longbing Cao
We present a self-supervised variational autoencoder (VAE) to jointly learn disentangled and dependent hidden factors and then enhance disentangled representation learning by a sel…