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Special Projects
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Type
Format
Created
Subject
Creator
Publisher
Contributor
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Machines People Use
Description:
10:50 - 11:00 a.m. Program Opens "And then..." Program Closes: "... Minnesota School of the Air."
Creator:
University of Minnesota. Radio Station KUOM
Created:
1976-02-19
Contributed By:
University of Minnesota Libraries, University Archives.
Last Updated:
2020-03-26
Machines People Use
Machinery demonstration
Description:
CCW representatives demonstrate how to oeprate a piece of machinery to a group of Wisconsin Essay contest visitors.
Created:
1945
Contributed By:
University of Minnesota Libraries, Immigration History Research Center Archives.
Last Updated:
2017-12-29
Machinery demonstration
Machinery, Dairy, Purity, copy
Description:
Thomas Sadler Roberts gave a series of talks and lectures on dairy farms in 1903-1904. This negatives was likely produced to create a lantern slide to illustrate his lecture.
Creator:
Bell Museum of Natural History
Created:
1903
Contributed By:
University of Minnesota Libraries, University Archives.
Page Count:
2
Last Updated:
2019-01-30
Machinery, Dairy, Purity, copy
Machine-Learning the String Theory Landscape: Beyond SageMath and Macaulay2
Description:
We present an overview of how SageMath and Macaulay2 have provided crucial computational help and insight to problems in string theory over the last decade. As concrete examples, we will use the Calabi-Yau landscape. In parallel, we propose a paradigm to machine-learn the ever-expanding databases which have emerged in mathematical physics, algeb...
Creator:
He, Yang-Hui (University of London)
Created:
2019-07-23
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Machine-Learning the String Theory Landscape: Beyond SageMath and Macaulay2
Machine Learning Techniques for High-Dimensional Optimal Transport
Description:
We present machine learning (ML) approaches for approximately solving optimal transport problems in the high-dimensional setting. Problems of this kind frequently arise in statistics, Bayesian inference, and generative modeling, yet progress has been limited due to the curse-of-dimensionality. As our learning framework tackles the optimal cont...
Creator:
Ruthotto, Lars (Emory University)
Created:
2021-04-20
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Machine Learning Techniques for High-Dimensional Optimal Transport
Machine Learning Models for Feature Selection and Classification of Traffic Anomalies
Description:
Traffic anomalies in communication networks greatly degrade network performance. In this talk, I will survey statistical and machine learning techniques that are used to classify and detect network anomalies such as Internet worms that affect performance of routing protocols. Various classification features are used to design anomaly detection m...
Creator:
Trajkovic, Ljiljana (Simon Fraser University)
Created:
2012-09-05
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2018-06-19
Machine Learning Models for Feature Selection and Classification of Traffic Anomalies
Machine Learning Methods for Solving High-dimensional Mean-field Game Systems
Description:
Mean-field games (MFG) is a framework to model and analyze huge populations of interacting agents that play non-cooperative differential games with applications in crowd motion, economics, finance, etc. Additionally, the PDE that arise in MFG have a rich mathematical structure and include those that appear in optimal transportation and density f...
Creator:
Nurbekyan, Levon (University of California, Los Angeles)
Created:
2020-11-03
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Machine Learning Methods for Solving High-dimensional Mean-field Game Systems
Machine Learning in Imaging and Inverse Problems: Where is it Going?
Description:
We consider the problem of learning models of scattering that decomposes scene returns into a set of scattering centers with limited persistence by utilizing sparsity of scattering coefficients. We consider both mono-static and bi-static radar collection geometries along wide-angle trajectories. The resulting sparse model can be interrogated at ...
Creator:
Bouman, Charles (Purdue University)
Created:
2018-10-23
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Machine Learning in Imaging and Inverse Problems: Where is it Going?
Machine Learning for Tomographic Imaging
Description:
Computer vision and image analysis are major application examples of deep learning. While computer vision and image analysis deal with existing images and produce features of these images (images to features), tomographic imaging produces images of multi-dimensional structures from experimentally measured “encoded” data as various tomographic fe...
Creator:
Wang, Ge (Rensselaer Polytechnic Institute)
Created:
2019-10-16
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2022-04-22
Machine Learning for Tomographic Imaging
Machine Learning for Background Estimation in Multispectral Imagery
Description:
We can improve the detection of targets and anomalies in a cluttered background by more effectively estimating that background. With a good estimate of what the target-free radiance or reflectance ought to be at a pixel, we have a point of comparison with what the measured value of that pixel actually happens to be. It is common to make this est...
Creator:
Theiler, James (Los Alamos National Laboratory)
Created:
2018-10-24
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Last Updated:
2019-02-04
Machine Learning for Background Estimation in Multispectral Imagery