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: "Lai, Chieh-Hsin"

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. ; ; (, International Conference on Artificial Intelligence and Statistics)
    Accepted Manuscript Full Text Available
  2. ; ; (, Eighth International Conference on Learning Representations)
    We propose a neural network for unsupervised anomaly detection with a novel robust subspace recovery layer (RSR layer). This layer seeks to extract the underlying subspace from a latent representation of the given data and removes outliers that lie away from this subspace. It is used within an autoencoder. The encoder maps the data into a latent space, from which the RSR layer extracts the subspace. The decoder then smoothly maps back the underlying subspace to a “manifold” close to the original inliers. Inliers and outliers are distinguished according to the distances between the original and mapped positions (small for inliers and large for outliers). Extensive numerical experiments with both image and document datasets demonstrate state-of-the-art precision and recall. 
    more » « less
    Accepted Manuscript Full Text Available
  3. ; ; ; ; (, ICML workshop on ML Interpretability for Scientific Discovery)
    In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output. When inverting such physical systems, i.e., solving the associated inverse problems, there is no unique solution. This causes fundamental difficulty in deploying the emerging end-to-end deep learning approach. Using the generalized phase retrieval problem as an illustrative example, we show that careful symmetry breaking on training data can help remove the difficulty and significantly improve the learning performance. We also extract and highlight the underlying mathematical principle of the proposed solution, which is directly applicable to other inverse problems. A full-length version of this paper can be found at https://arxiv.org/abs/2003.09077. 
    more » « less
    Accepted Manuscript Full Text Available