The GRASTAcam code is availabile for download here. This code uses OpenCV to run Grasta using the camera on your computer. It was written by Arthur Szlam (with makefile by Jia Xu, Thanks Jia!) in C using the Intel MKL library.
November 30, 2012
The GRASTAcam code is availabile for download here. This code uses OpenCV to run Grasta using the camera on your computer. It was written by Arthur Szlam (with makefile by Jia Xu, Thanks Jia!) in C using the Intel MKL library.
I am honored to have received an MLK Spirit Award from the Michigan College of Engineering. These awards are given to university members who exemplify the leadership and vision of Reverend Dr. Martin Luther King, Jr. through their commitment to social justice, diversity, equity, and inclusion. That commitment is a very high priority for me, so I am grateful that others have felt the impact of my actions. https://ece.engin.umich.edu/stories/laura-balzano-receives-2023-mlk-spirit-award
I’m excited that our results for the K-Subspaces algorithm were accepted to ICML. My postdoc Peng Wang will be presenting his excellent work; you may read the paper here or attend his session if you are interested. K-Subspaces (KSS) is a natural generalization of K-Means to higher dimensional centers, originally proposed by Bradley and Mangasarian in 2000. Peng not only showed that KSS converges locally, but that a simple spectral initialization guarantees a close-enough initialization in the case of data drawn randomly from arbitrary subspaces. This makes a giant step in a line of questioning that has been open for more than 20 years. Great work Peng!
Hessam Mahdavifar and I have been awarded funds from the Department of Energy to study sketching in the context of non-real-valued data. Randomized sketching and subsampling algorithms are revolutionizing the data processing pipeline by allowing significant compression of redundant information. However, current research assumes input data are real-valued, when many sensing, storage, and computation modalities in scientific and technological applications are best modeled mathematically as other types of data, including discrete-valued data and ordinal or categorical data, among others. You can read about the project here and read a Q&A here that was highlighted on the DoE office of science website. We are excited about the opportunity to expand in this new direction!
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