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Deep Machine Learning for Machine Performance and Damage Prediction
08 Aug 2018 | Contributor(s):: Elijah Reber, Nickolas D Winovich, Guang Lin
Deep learning has provided opportunities for advancement in many fields. One such opportunity is being able to accurately predict real world events. Ensuring proper motor function and being able to predict energy output is a valuable asset for owners of wind turbines. In this paper, we look at...
Is More Data Better Than Better Algorithms in Machine Learning?
08 Jun 2018 |
Posted by Cogito Tech LLC
Yes in machine learning more data is always better than better algorithms. Actually, the quality of data defines how the inputs will work in machine learning training and output would be exactly...
Jeremy Seiji Marquardt
Applying Machine Learning to Computational Chemistry: Can We Predict Molecular Properties Faster without Compromising Accuracy?
14 Aug 2017 | Contributor(s):: Hanjing Xu, Pradeep Kumar Gurunathan
Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions...
Predicting Locations of Pollution Sources using Convolutional Neural Networks
07 Aug 2017 | Contributor(s):: Yiheng Chi, Nickolas D Winovich, Guang Lin
Pollution is a severe problem today, and the main challenge in water pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the locations of pollution sources given diffusion information of pollution in the form of array or...
S Kiran Kadam
IPython Notebooks for Machine Learning
21 May 2017 |
Posted by Tanya Faltens
Model Selection Using Gaussian Mixture Models and Parallel Computing
20 Jul 2016 | | Contributor(s):: Tian Qiu, Yiyi Chen, Georgios Karagiannis, Guang Lin
Model Selection Using Gaussian Mixture Models
Juan Sebastian Martinez
Gaussian process regression in 1D
26 Nov 2014 | | Contributor(s):: Ilias Bilionis, Alejandro Strachan, Benjamin P Haley, Martin Hunt, Rohit Kaushal Tripathy, Sam Reeve
Use Gaussian processes to represent x-y data
Rohit Kaushal Tripathy
German Felipe Giraldo