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 3

Search for: All records

Award ID contains: 1912280

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. ; (, Annals of the New York Academy of Sciences)
    Abstract Visual perception involves the rapid formation of a coarse image representation at the onset of visual processing, which is iteratively refined by late computational processes. These early versus late time windows approximately map onto feedforward and feedback processes, respectively. State‐of‐the‐art convolutional neural networks, the main engine behind recent machine vision successes, are feedforward architectures. Their successes and limitations provide critical information regarding which visual tasks can be solved by purely feedforward processes and which require feedback mechanisms. We provide an overview of recent work in cognitive neuroscience and machine vision that highlights the possible role of feedback processes for both visual recognition and beyond. We conclude by discussing important open questions for future research. 
    more » « less
  2. (, Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available
  3. (, Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available
  4. (, In: Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available
  5. (, Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available
  6. (, International Conference on Machine Learning (ICML))
    Accepted Manuscript Full Text Available
  7. (, International Conference on Learning Representations (ICLR))
    Accepted Manuscript Full Text Available
  8. (, Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available
  9. ; ; ; (, Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available
  10. ; ; ; (, Neural Information Processing Systems (NeurIPS))
    Accepted Manuscript Full Text Available