Attention:The NSF Public Access Repository (PAR) system and access will be unavailable from 5:00 PM ET until 8:00 PM ET on Friday, September 11 due to maintenance. We apologize for the inconvenience.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • Home
  • Search Results
  • Page 1 of 5

Search for: All records

Creators/Authors contains: "Zhao, Han"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. ; ; (, Transactions on machine learning research)
    Free, publicly-accessible full text available June 18, 2027
  2. ; ; ; (, AISTATS)
    Free, publicly-accessible full text available May 31, 2027
  3. ; ; ; ; ; ; ; ; ; (, International Conference on Machine Learning)
    Free, publicly-accessible full text available July 1, 2027
  4. ; ; ; ; (, Transactions on machine learning research)
    Free, publicly-accessible full text available March 1, 2027
  5. ; (, Advances in Neural Information Processing Systems (NeurIPS))
    Free, publicly-accessible full text available December 1, 2026
  6. ; ; ; ; ; ; ; (, Transactions on machine learning research)
    Yao, Yu (Ed.)
    We introduced Filter-VAE, a weakly supervised variational autoencoder (VAE) that introduces a filter-based adaptive swapping strategy to learn stable and meaningful disentangled representations. Specifically, a relevance filter removes semantically meaningless latent factors, while an adaptive swapping filter exchanges those latent factors that have reached stability. With these two filters, Filter-VAE adaptively swaps only stable and semantically aligned latent factors, leading to robust and meaningful representations. Experimental results demonstrate that Filter-VAE achieves strong disentanglement performance with reduced supervision and delivers remarkable robustness against diverse adversarial attacks and corruptions. 
    more » « less
    Free, publicly-accessible full text available January 31, 2027
  7. ; ; ; ; ; ; ; ; (, Environmental Science & Technology)
    Free, publicly-accessible full text available April 7, 2027
  8. ; ; ; ; (, Advances in Neural Information Processing Systems)
    Free, publicly-accessible full text available December 2, 2026
  9. ; ; ; ; (, Advances in Neural Information Processing Systems (NeurIPS))
    Free, publicly-accessible full text available December 1, 2026
  10. ; ; ; ; ; (, Advances in Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track)
    Free, publicly-accessible full text available December 1, 2026