8 citations · 18 across the 8 of their papers we have counts for
8 papers
T1: One-to-One Channel-Head Binding for Multivariate Time-Series Imputation
Dongik Park, Hyunwoo Ryu, Suahn Bae +2
Imputing missing values in multivariate time series remains challenging, especially under diverse missing patterns and heavy missingness. Existing methods suffer from suboptimal pe…
SleepMaMi: A Universal Sleep Foundation Model for Integrating Macro- and Micro-structures
Keondo Park, Younghoon Na, Yourim Choi +3
While the shift toward unified foundation models has revolutionized many deep learning domains, sleep medicine remains largely restricted to task-specific models that focus on loca…
AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shift
Eunsu Baek, Keondo Park, Jeonggil Ko +3
Current AI advances largely rely on scaling neural models and expanding training datasets to achieve generalization and robustness. Despite notable successes, this paradigm incurs…
PointSplit: Towards On-device 3D Object Detection with Heterogeneous Low-power Accelerators
Keondo Park, You Rim Choi, Inhoe Lee +1
Running deep learning models on resource-constrained edge devices has drawn significant attention due to its fast response, privacy preservation, and robust operation regardless of…
Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domains
Eunsu Baek, Keondo Park, Jiyoon Kim +1
Computer vision applications predict on digital images acquired by a camera from physical scenes through light. However, conventional robustness benchmarks rely on perturbations in…
Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning
Jun-Yeong Moon, Keon-Hee Park, Jung Uk Kim +1
Continual learning aims to learn a model from a continuous stream of data, but it mainly assumes a fixed number of data and tasks with clear task boundaries. However, in real-world…
Federated Semi-Supervised Learning with Prototypical Networks
Woojung Kim, Keondo Park, Kihyuk Sohn +2
With the increasing computing power of edge devices, Federated Learning (FL) emerges to enable model training without privacy concerns. The majority of existing studies assume the…
Real-time Mask Detection on Google Edge TPU
Keondo Park, Wonyoung Jang, Woochul Lee +4
After the COVID-19 outbreak, it has become important to automatically detect whether people are wearing masks in order to reduce risk of front-line workers. In addition, processing…