Search Results for author: Mohammad Khalil

Found 7 papers, 1 papers with code

Learning Analytics in Massive Open Online Courses

no code implementations17 Feb 2018 Mohammad Khalil

Educational technology has obtained great importance over the last fifteen years.

A heteroencoder architecture for prediction of failure locations in porous metals using variational inference

no code implementations31 Jan 2022 Wyatt Bridgman, Xiaoxuan Zhang, Greg Teichert, Mohammad Khalil, Krishna Garikipati, Reese Jones

In this work we employ an encoder-decoder convolutional neural network to predict the failure locations of porous metal tension specimens based only on their initial porosities.

Variational Inference

Will ChatGPT get you caught? Rethinking of Plagiarism Detection

no code implementations8 Feb 2023 Mohammad Khalil, Erkan Er

To this end, two popular plagiarism detection tools were used to evaluate the originality of 50 essays generated by ChatGPT on various topics.

Robust scalable initialization for Bayesian variational inference with multi-modal Laplace approximations

no code implementations12 Jul 2023 Wyatt Bridgman, Reese Jones, Mohammad Khalil

In this work, we propose a method for constructing an initial Gaussian mixture model approximation that can be used to warm-start the iterative solvers for variational inference.

Variational Inference

Transfer learning for predicting source terms of principal component transport in chemically reactive flow

no code implementations1 Dec 2023 Ki Sung Jung, Tarek Echekki, Jacqueline H. Chen, Mohammad Khalil

The performance of the reduced-order model with a sparse dataset is found to be remarkably enhanced if the training of the ANN model is restricted by a regularization term that controls the degree of knowledge transfer from source to target tasks.

Transfer Learning

Scaling While Privacy Preserving: A Comprehensive Synthetic Tabular Data Generation and Evaluation in Learning Analytics

1 code implementation12 Jan 2024 Qinyi Liu, Mohammad Khalil, Ronas Shakya, Jelena Jovanovic

To address these gaps, we propose a comprehensive evaluation of synthetic data, which encompasses three dimensions of synthetic data quality, namely resemblance, utility, and privacy.

Privacy Preserving Synthetic Data Generation

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