Search Results for author: Muhammad Shayan

Found 4 papers, 1 papers with code

Few-shot Question Generation for Personalized Feedback in Intelligent Tutoring Systems

no code implementations • 8 Jun 2022 • Devang Kulshreshtha, Muhammad Shayan, Robert Belfer, Siva Reddy, Iulian Vlad Serban, Ekaterina Kochmar

Our personalized feedback can pinpoint correct and incorrect or missing phrases in student answers as well as guide them towards correct answer by asking a question in natural language.

Generative Question Answering Question Generation +3

Paper
Add Code

A New Era: Intelligent Tutoring Systems Will Transform Online Learning for Millions

no code implementations • 3 Mar 2022 • Francois St-Hilaire, Dung Do Vu, Antoine Frau, Nathan Burns, Farid Faraji, Joseph Potochny, Stephane Robert, Arnaud Roussel, Selene Zheng, Taylor Glazier, Junfel Vincent Romano, Robert Belfer, Muhammad Shayan, Ariella Smofsky, Tommy Delarosbil, Seulmin Ahn, Simon Eden-Walker, Kritika Sony, Ansona Onyi Ching, Sabina Elkins, Anush Stepanyan, Adela Matajova, Victor Chen, Hossein Sahraei, Robert Larson, Nadia Markova, Andrew Barkett, Laurent Charlin, Yoshua Bengio, Iulian Vlad Serban, Ekaterina Kochmar

AI-powered learning can provide millions of learners with a highly personalized, active and practical learning experience, which is key to successful learning.

Active Learning Multiple-choice

Paper
Add Code

Comparative Study of Learning Outcomes for Online Learning Platforms

no code implementations • 15 Apr 2021 • Francois St-Hilaire, Nathan Burns, Robert Belfer, Muhammad Shayan, Ariella Smofsky, Dung Do Vu, Antoine Frau, Joseph Potochny, Farid Faraji, Vincent Pavero, Neroli Ko, Ansona Onyi Ching, Sabina Elkins, Anush Stepanyan, Adela Matajova, Laurent Charlin, Yoshua Bengio, Iulian Vlad Serban, Ekaterina Kochmar

Personalization and active learning are key aspects to successful learning.

Active Learning Multiple-choice

Paper
Add Code

Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning

2 code implementations • 24 Nov 2018 • Muhammad Shayan, Clement Fung, Chris J. M. Yoon, Ivan Beschastnikh

Federated Learning is the current state of the art in supporting secure multi-party machine learning (ML): data is maintained on the owner's device and the updates to the model are aggregated through a secure protocol.

BIG-bench Machine Learning Federated Learning +1

100

Paper
Code

Cannot find the paper you are looking for? You can Submit a new open access paper.