no code implementations • 20 Feb 2024 • Hyolim Jeon, Dongje Yoo, Daeun Lee, Sejung Son, Seungbae Kim, Jinyoung Han
Despite the increasing demand for AI-based mental health monitoring tools, their practical utility for clinicians is limited by the lack of interpretability. The CLPsych 2024 Shared Task (Chim et al., 2024) aims to enhance the interpretability of Large Language Models (LLMs), particularly in mental health analysis, by providing evidence of suicidality through linguistic content.
1 code implementation • 18 Oct 2023 • Daeun Lee, Sejung Son, Hyolim Jeon, Seungbae Kim, Jinyoung Han
By learning the correlation between the speech and gesture modalities for each aphasia type, our model can generate textual representations sensitive to gesture information, leading to accurate aphasia type detection.
1 code implementation • 3 Jul 2023 • Daeun Lee, Sejung Son, Hyolim Jeon, Seungbae Kim, Jinyoung Han
Bipolar disorder (BD) is closely associated with an increased risk of suicide.
no code implementations • 4 Apr 2023 • Seungbae Kim, Jyun-Yu Jiang, Jinyoung Han, Wei Wang
In this paper, we propose InfluencerRank that ranks influencers by their effectiveness based on their posting behaviors and social relations over time.
1 code implementation • 22 Jul 2022 • Xiaofeng Lin, Seungbae Kim, Jungseock Joo
Existing pruning techniques preserve deep neural networks' overall ability to make correct predictions but may also amplify hidden biases during the compression process.
1 code implementation • CVPR 2022 • Yu Yang, Seungbae Kim, Jungseock Joo
We also demonstrate a novel application of our method for unsupervised dataset bias analysis which allows us to automatically discover hidden biases in datasets or compare different subsets without using additional labels.