no code implementations • EACL (BEA) 2021 • Debajyoti Datta, Maria Phillips, James P. Bywater, Jennifer Chiu, Ginger S. Watson, Laura Barnes, Donald Brown
Conversational agents and assistants have been used for decades to facilitate learning.
no code implementations • 10 Sep 2022 • Shashwat Kumar, Robert Gutierez, Debajyoti Datta, Sarah Tolman, Allison McCrady, Silvia Blemker, Rebecca J. Scharf, Laura Barnes
The proposed metric is invariant to confounding factors, such as phase variability, while suggesting several clinically relevant insights.
no code implementations • 9 Nov 2020 • Jinghe Zhang, Kamran Kowsari, Mehdi Boukhechba, James Harrison, Jennifer Lobo, Laura Barnes
Chronic kidney disease (CKD) is a gradual loss of renal function over time, and it increases the risk of mortality, decreased quality of life, as well as serious complications.
1 code implementation • 28 Oct 2020 • Mehrdad Fazli, Kamran Kowsari, Erfaneh Gharavi, Laura Barnes, Afsaneh Doryab
We evaluate our model on the Extrasensory dataset; a dataset collected in the wild and containing data from smartphones and smartwatches.
no code implementations • 23 Oct 2020 • Debajyoti Datta, Maria Phillips, Jennifer Chiu, Ginger S. Watson, James P. Bywater, Laura Barnes, Donald Brown
We propose using probabilistic modeling of annotator labeling to generate active learning examples to further label the data.
no code implementations • 14 Oct 2020 • Debajyoti Datta, Shashwat Kumar, Laura Barnes, Tom Fletcher
Using our approach, we explore difficult examples for several deep learning architectures.
no code implementations • 12 Oct 2020 • Sonia Baee, Mark Rucker, Anna Baglione, Mawulolo K. Ameko, Laura Barnes
Virtual coaching has rapidly evolved into a foundational component of modern clinical practice.
2 code implementations • ICCV 2021 • Sonia Baee, Erfan Pakdamanian, Inki Kim, Lu Feng, Vicente Ordonez, Laura Barnes
Inspired by human visual attention, we propose a novel inverse reinforcement learning formulation using Maximum Entropy Deep Inverse Reinforcement Learning (MEDIRL) for predicting the visual attention of drivers in accident-prone situations.