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Zhangkai Wu

18 papers hereh-index 7285 citations25 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author7
  • middle author8
  • last author2

Across the 17 of 18 papers where every author was matched, so the position is known.

fields
  • cs.LG7
  • cs.CV5
  • cs.CL2
  • cs.SD2
  • cs.NE1
  • stat.ML1
same name
  • Zhangkai Wu — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20192026
most citedeVAE: Evolutionary Variational Autoencoder

34 citations · 69 across the 16 of their papers we have counts for

collaborators
Showing 2024Show all

4 papers · 1 filter

cs.LG2024★ 2 cited

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…

cs.LG2024★ 25 cited

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…

cs.LG2024

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…

cs.LG2024★ 2 cited

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.