1 code implementation • 1 Apr 2024 • Taeckyung Lee, Sorn Chottananurak, Taesik Gong, Sung-Ju Lee
We propose the prediction disagreement as the accuracy estimate, calculated by comparing the target model prediction with dropout inferences.
no code implementations • 3 Mar 2024 • Adiba Orzikulova, Han Xiao, Zhipeng Li, Yukang Yan, Yuntao Wang, Yuanchun Shi, Marzyeh Ghassemi, Sung-Ju Lee, Anind K Dey, Xuhai "Orson" Xu
Participants preferred the adaptive interventions and rated the system highly on intervention time accuracy, effectiveness, and level of trust.
no code implementations • 25 Oct 2023 • Jaemin Shin, Hyungjun Yoon, SeungJoo Lee, Sungjoon Park, Yunxin Liu, Jinho D. Choi, Sung-Ju Lee
Psychiatrists diagnose mental disorders via the linguistic use of patients.
1 code implementation • NeurIPS 2023 • Taesik Gong, Yewon Kim, Taeckyung Lee, Sorn Chottananurak, Sung-Ju Lee
To address this problem, we present Screening-out Test-Time Adaptation (SoTTA), a novel TTA algorithm that is robust to noisy samples.
no code implementations • 5 Apr 2023 • Yewon Kim, Mina Lee, Donghwi Kim, Sung-Ju Lee
We highlight the challenges faced by non-native speakers when using AI writing assistants to paraphrase text.
no code implementations • 2 Sep 2022 • Hyungjun Yoon, Hyeongheon Cha, Hoang C. Nguyen, Taesik Gong, Sung-Ju Lee
Our evaluation with four different IMU sensing tasks shows that IMG2IMU outperforms the baselines pre-trained on sensor data by an average of 9. 6%p F1-score, illustrating that vision knowledge can be usefully incorporated into IMU sensing applications where only limited training data is available.
1 code implementation • 10 Aug 2022 • Taesik Gong, Jongheon Jeong, Taewon Kim, Yewon Kim, Jinwoo Shin, Sung-Ju Lee
Test-time adaptation (TTA) is an emerging paradigm that addresses distributional shifts between training and testing phases without additional data acquisition or labeling cost; only unlabeled test data streams are used for continual model adaptation.
1 code implementation • 5 Jan 2022 • Jaemin Shin, Yuanchun Li, Yunxin Liu, Sung-Ju Lee
Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data.
no code implementations • 22 Nov 2021 • Taesik Gong, Yewon Kim, Adiba Orzikulova, Yunxin Liu, Sung Ju Hwang, Jinwoo Shin, Sung-Ju Lee
However, various factors such as different users, devices, and environments impact the performance of such applications, thus making the domain shift (i. e., distributional shift between the training domain and the target domain) a critical issue in mobile sensing.