Explore projects
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Framework for perturbation analysis on Spark. Used for computational fact-checking/finding/discovery tasks.
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Winning Solution of the NeurIPS 2020 Competition on Predicting Generalization in Deep Learning
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Optimizing PAC-Bayes bounds for Stochastic Neural Networks with Gaussian weights
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Experiments to see if removing the part of the gradient that lies in the dominating eigenspace for hessian can make the optimization result better off.
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Skeleton code for the CompSci 316 (Database Design) undergraduate course project. This course project is the standard version.
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