2 citations · 3 across the 8 of their papers we have counts for
10 papers
Later Is Better: Token Reduction for ViTs Under Distribution Shift
Hyeongheon Cha, Hyungjun Yoon, Sung-Ju Lee
Training-free token reduction accelerates vision transformers by removing redundant tokens across layers, recovering most of the original accuracy at a fraction of the compute. The…
TimeNet: An Extensible Unified Data Infrastructure for Next-Generation Temporal Foundation Models
Martin Maritsch, Timo Stoffregen, Thomas Kaar +36
Temporal Foundation Models (TFMs) aim to generalize across domains, datasets, and tasks. Yet, their development remains constrained by fragmented, task-specific data formats, annot…
Wearable Foundation Models Should Go Beyond Static Encoders
Yu Yvonne Wu, Yuwei Zhang, Hyungjun Yoon +8
Wearable foundation models (WFMs), trained on large volumes of data collected by affordable, always-on devices, have demonstrated strong performance on short-term, well-defined hea…
ConSensus: Multi-Agent Collaboration for Multimodal Sensing
Hyungjun Yoon, Mohammad Malekzadeh, Sung-Ju Lee +2
Large language models (LLMs) are increasingly grounded in sensor data to perceive and reason about human physiology and the physical world. However, accurately interpreting heterog…
Beyond Hearing: Learning Task-Agnostic ExG Representations from Earphones via Physiology-Informed Tokenization
Hyungjun Yoon, Seungjoo Lee, Yu Yvonne Wu +11
Electrophysiological (ExG) signals offer valuable insights into human physiology, yet building foundation models that generalize across everyday tasks remains challenging due to tw…
By My Eyes: Grounding Multimodal Large Language Models with Sensor Data via Visual Prompting
Hyungjun Yoon, Biniyam Aschalew Tolera, Taesik Gong +2
Large language models (LLMs) have demonstrated exceptional abilities across various domains. However, utilizing LLMs for ubiquitous sensing applications remains challenging as exis…
Recover as It is Designed to Be: Recovering from Compatibility Mobile App Crashes by Reusing User Flows
Donghwi Kim, Hyungjun Yoon, Chang Min Park +4
Android OS is severely fragmented by API updates and device vendors' OS customization, creating a market condition where vastly different OS versions coexist. This gives rise to co…
SelfReplay: Adapting Self-Supervised Sensory Models via Adaptive Meta-Task Replay
Hyungjun Yoon, Jaehyun Kwak, Biniyam Aschalew Tolera +5
Self-supervised learning has emerged as a method for utilizing massive unlabeled data for pre-training models, providing an effective feature extractor for various mobile sensing a…
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning
Jaemin Shin, Hyungjun Yoon, Seungjoo Lee +4
Psychiatrists diagnose mental disorders via the linguistic use of patients. Still, due to data privacy, existing passive mental health monitoring systems use alternative features s…
IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications
Hyungjun Yoon, Hyeongheon Cha, Hoang C. Nguyen +2
Pre-training representations acquired via self-supervised learning could achieve high accuracy on even tasks with small training data. Unlike in vision and natural language process…