skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

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 1

Search for: All records

Creators/Authors contains: "Yu, Qixuan"

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. ; ; ; ; ; (, DAC '22: Proceedings of the 59th ACM/IEEE Design Automation Conference)
    Contrastive learning learns visual representations by enforcing feature consistency under different augmented views. In this work, we explore contrastive learning from a new perspective. Interestingly, we find that quantization, when properly engineered, can enhance the effectiveness of contrastive learning. To this end, we propose a novel contrastive learning framework, dubbed Contrastive Quant, to encourage feature consistency under both differently augmented inputs via various data transformations and differently augmented weights/activations via various quantization levels. Extensive experiments, built on top of two state-of-the-art contrastive learning methods SimCLR and BYOL, show that Contrastive Quant consistently improves the learned visual representation. 
    more » « less
    Full Text Available 
  2. ; ; ; ; ; ; (, Proceedings of the 43rd ACM SIGPLAN International Conference on Programming Language Design and Implementation)
    Full Text Available 
  3. ; ; ; ; (, The 54th IEEE/ACM International Symposium on Microarchitecture 2021)
    null (Ed.)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; (, The 54th IEEE/ACM International Symposium on Microarchitecture (MICRO 2021), 2021)
    null (Ed.)
    Accepted Manuscript Full Text Available
  5. ; ; ; ; ; (, 35th Conference on Neural Information Processing Systems (NeurIPS 2021))
    Full Text Available 
  6. ; ; ; ; ; ; ; ; (, The 9th International Conference on Learning Representations 2021 (ICLR 2021))
    null (Ed.)
    Accepted Manuscript Full Text Available