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Implemented a CoGAN (coupled GAN) architecture to generate paintings combining genres from the WikiArt dataset. Coupled two GANs by weight sharing between initial and final layers of generators to produce combination of different genres being processed by each one. Used various forms of regularization to improve performance and stabilize training.
Implemented various machine learning algorithms, including SVMs, logistic regression, and CNNs for predicting future activities from the Extrasensory Dataset. Constructed a modified version of the dataset to meet the task definition by sampling and modifying the original semi-structured data cases to perform supervised learning.Performed dedicated data processing, developing a pipeline for missing data imputation and dimensionality reduction.
Built a language model created from tweets from the Hate Speech and Offensive Language dataset using Universal language model fine-tuning, and constructed a classifier with 98% testing accuracy using the language model. Used the tweet-based classifier to detect sexism in amateur fiction from the subreddit r/WritingPrompts, and studied false positives and negatives, such as in the context of sarcasm
Published in Proceeding of the International Conference on Computer Networks, Big Data and IoT (ICCBI - 2018), 2018
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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