Source code "Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem." @ CVPR2020
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Updated
Dec 8, 2022 - Python
Source code "Unsupervised Model Personalization while Preserving Privacy and Scalability: An Open Problem." @ CVPR2020
This project demonstrates the use of generic bi-directional LSTM models for predicting importance of words in a spoken dialgoue for understanding its meaning. The model operates on human-annotated corpus of word importance for its training and evaluation. The corpus can be downloaded from: http://latlab.ist.rit.edu/lrec2018
Thesis titled "Geospatial Semantic Pattern Recognition in Volunteered Geographic Data Using the Random forest Algorithm" for the degree of Masters of Spatial Analysis at Ryerson University in 2016
Scraping Rotten Tomatoes to Uncover Hidden Trends
An implementation of a decision tree classifier to predict the most present age in each urban area in the data set.
Python module to calculate local variable importance with the global model
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