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Found 9 papers, 4 papers with code
DAPPER: Label-Free Performance Estimation after Personalization for Heterogeneous Mobile Sensing
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.
FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients
Federated Learning (FL) trains a machine learning model on distributed clients without exposing individual data.
NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation
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.
IMG2IMU: Translating Knowledge from Large-Scale Images to IMU Sensing Applications
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.
Towards Explainable AI Writing Assistants for Non-native English Speakers
We highlight the challenges faced by non-native speakers when using AI writing assistants to paraphrase text.
SoTTA: Robust Test-Time Adaptation on Noisy Data Streams
To address this problem, we present Screening-out Test-Time Adaptation (SoTTA), a novel TTA algorithm that is robust to noisy samples.
FedTherapist: Mental Health Monitoring with User-Generated Linguistic Expressions on Smartphones via Federated Learning
Psychiatrists diagnose mental disorders via the linguistic use of patients.
Time2Stop: Adaptive and Explainable Human-AI Loop for Smartphone Overuse Intervention
Participants preferred the adaptive interventions and rated the system highly on intervention time accuracy, effectiveness, and level of trust.
AETTA: Label-Free Accuracy Estimation for Test-Time Adaptation
We propose the prediction disagreement as the accuracy estimate, calculated by comparing the target model prediction with dropout inferences.
