The web has a vast wealth of information about various types of entities such as businesses (e.g., address, phone, category, hours of operation), products, books, doctors, etc. distributed over a very large number of web sites. Extracting this information from the websites can help us create extensive databases of the entities. These databases c...
Creator:
Keerthi, Sathiya (Yahoo! Inc.)
Created:
2011-02-11
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In this talk I will discuss the novel experimental designs for large-scale multiple hypothesis testing problems. Testing to determine which genes are differentially expressed in a certain disease is a classic instance of multiple testing in medical informatics. Tremendous progress has been made in high-dimensional inference and testing problems ...
Creator:
Nowak, Robert (University of Wisconsin, Madison)
Created:
2011-11-16
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Large-scale optimization, sometimes called 'inverse design' or 'topology optimization,' involves the computational design of new structures that maximize performance of a device, with so many parameters (hundreds, thousands, or more) that the computer is free to 'discover' entirely new geometries rather than simply tweaking a few parameters of a...
Creator:
Johnson, Steven G. (Massachusetts Institute of Technology)
Created:
2017-04-25
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
We focus on developing a novel scalable graph-based semi-supervised learning (SSL) method for a small number of labeled data and a large amount of unlabeled data. Due to the lack of labeled data and the availability of large-scale unlabeled data, existing SSL methods usually encounter either suboptimal performance because of an improper graph or...
Creator:
Wang, Li (University of Texas at Arlington)
Created:
2020-10-06
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Subspace clustering refers to the problem of clustering data drawn from a union of low-dimensional subspaces of a high-dimensional space. State-of-the-art subspace clustering methods are based on expressing each data point as a linear combination of other data points while regularizing the matrix of coefficients with the L1, L2, or nuclear norms...
Creator:
Vidal, René (Johns Hopkins University)
Created:
2016-09-16
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
This photograph is one of a set of three. From the creator: "These photos were taken on April 7, 2020. They show signs teachers made and hung in the windows of Lester Park School for their students. These pictures were used in posts on the Department of Education Instagram, Twitter, and Facebook pages with the text "Heartwarming messages from t...
Creator:
Vigen, Brianne
Created:
2020-04-07
Contributed By:
Archives and Special Collections, Kathryn A. Martin Library, University of Minnesota Duluth