2020 journal article

Going deeper: Automatic short-answer grading by combining student and question models

USER MODELING AND USER-ADAPTED INTERACTION, 30(1), 51–80.

By: Y. Zhang n, C. Lin n & M. Chi n

author keywords: Automatic short-answer grading; Machine learning; Deep belief network
TL;DR: Overall, the results on a real-world corpus demonstrate that 1) leveraging student and question models to the conventional answer-based approach can greatly enhance the performance of ASAG, and 2) deep learning models such as DBN can be productively applied to the task of ASAGs. (via Semantic Scholar)
UN Sustainable Development Goal Categories
4. Quality Education (OpenAlex)
Source: Web Of Science
Added: March 30, 2020

2017 conference paper

LSTM for septic shock: Adding unreliable labels to reliable predictions

2017 IEEE International Conference on Big Data (Big Data), 1233–1242.

By: Y. Zhang n, C. Lin n, M. Chi n, J. Ivy n, M. Capan* & J. Huddleston*

TL;DR: A generic framework to predict septic shock based on Long-Short Term Memory (LSTM) model, which is capable of memorizing temporal dependencies over a long period, is proposed and the robustness of the method is validated using three sets of clinician-proposed adjusted ground-truth labels. (via Semantic Scholar)
Source: NC State University Libraries
Added: August 6, 2018

Citation Index includes data from a number of different sources. If you have questions about the sources of data in the Citation Index or need a set of data which is free to re-distribute, please contact us.

Certain data included herein are derived from the Web of Science© and InCites© (2024) of Clarivate Analytics. All rights reserved. You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.