Robust Educational Dialogue Act Classifiers with Low-Resource and Imbalanced Datasets

no code implementations • 15 Apr 2023 • Jionghao Lin, Wei Tan, Ngoc Dang Nguyen, David Lang, Lan Du, Wray Buntine, Richard Beare, Guanliang Chen, Dragan Gasevic

We note that many prior studies on classifying educational DAs employ cross entropy (CE) loss to optimize DA classifiers on low-resource data with imbalanced DA distribution.

Does Informativeness Matter? Active Learning for Educational Dialogue Act Classification

no code implementations • 12 Apr 2023 • Wei Tan, Jionghao Lin, David Lang, Guanliang Chen, Dragan Gasevic, Lan Du, Wray Buntine

Then, the study investigates how the AL methods can select informative samples to support DA classifiers in the AL sampling process.

Active Learning Dialogue Act Classification +2

GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging

1 code implementation • 26 Feb 2021 • Sarthak Pati, Siddhesh P. Thakur, İbrahim Ethem Hamamcı, Ujjwal Baid, Bhakti Baheti, Megh Bhalerao, Orhun Güley, Sofia Mouchtaris, David Lang, Spyridon Thermos, Karol Gotkowski, Camila González, Caleb Grenko, Alexander Getka, Brandon Edwards, Micah Sheller, Junwen Wu, Deepthi Karkada, Ravi Panchumarthy, Vinayak Ahluwalia, Chunrui Zou, Vishnu Bashyam, Yuemeng Li, Babak Haghighi, Rhea Chitalia, Shahira Abousamra, Tahsin M. Kurc, Aimilia Gastounioti, Sezgin Er, Mark Bergman, Joel H. Saltz, Yong Fan, Prashant Shah, Anirban Mukhopadhyay, Sotirios A. Tsaftaris, Bjoern Menze, Christos Davatzikos, Despina Kontos, Alexandros Karargyris, Renato Umeton, Peter Mattson, Spyridon Bakas

Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities.

Brain Segmentation Data Augmentation +2

Modeling Student Response Times: Towards Efficient One-on-one Tutoring Dialogues

no code implementations • WS 2018 • Luciana Benotti, Jayadev Bhaskaran, Sigtryggur Kjartansson, David Lang

Knowing how long it would normally take a student to respond to different types of questions could help tutors optimize their own time while answering multiple dialogues concurrently, as well as deciding when to prompt a student again.

Math