219 citations · 249 across the 18 of their papers we have counts for
10 papers · 1 filter
Time is Not Enough: Time-Frequency based Explanation for Time-Series Black-Box Models
Hyunseung Chung, Sumin Jo, Yeonsu Kwon +1
Despite the massive attention given to time-series explanations due to their extensive applications, a notable limitation in existing approaches is their primary reliance on the ti…
Recent Advances, Applications, and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2023 Symposium
Hyewon Jeong, Sarah Jabbour, Yuzhe Yang +40
The third ML4H symposium was held in person on December 10, 2023, in New Orleans, Louisiana, USA. The symposium included research roundtable sessions to foster discussions between…
Self-Supervised Contrastive Learning for Long-term Forecasting
Junwoo Park, Daehoon Gwak, Jaegul Choo +1
Long-term forecasting presents unique challenges due to the time and memory complexity of handling long sequences. Existing methods, which rely on sliding windows to process long s…
Multimodal Transformer With a Low-Computational-Cost Guarantee
Sungjin Park, Edward Choi
Transformer-based models have significantly improved performance across a range of multimodal understanding tasks, such as visual question answering and action recognition. However…
Learning under Label Noise through Few-Shot Human-in-the-Loop Refinement
Aaqib Saeed, Dimitris Spathis, Jungwoo Oh +2
Wearable technologies enable continuous monitoring of various health metrics, such as physical activity, heart rate, sleep, and stress levels. A key challenge with wearable data is…
Learning Missing Modal Electronic Health Records with Unified Multi-modal Data Embedding and Modality-Aware Attention
Kwanhyung Lee, Soojeong Lee, Sangchul Hahn +4
Electronic Health Record (EHR) provides abundant information through various modalities. However, learning multi-modal EHR is currently facing two major challenges, namely, 1) data…