Latest

• Proximity-Based Active Learning for Eating Moment Recognition in WearableSystems
• ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition.
• Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
• Transfer Learning via Learning to Transfer
• Master Defense Practice III
• Defense Practice II.
• Defense Practice I.
• Introduction to Long Short Term Memory Networks
• A Closed-loop Deep Learning Architecture for Robust Activity Recognition using Wearable Sensors
• Label Propagation: An Unsupervised Similarity Based Method for Integrating New Sensors in Activity Recognition Systems
• Collegial Activity Learning Between Heterogeneous Sensors
• Learning from less for better: semi-supervised activity recognition via shared structure discovery
• Reliable and reconfigurable framework for physical activity monitoring
• Assignment Problem (Part II)
• Assignment Problem (Part I)
• Hypothesis Testing
• Network-based relational knowledge transfer
• NetSimile: A Scalable Approach to Size-Independent Network Similarity
• Randomized 3-approximation MAX-CNF
• Learn-on-the-go: Autonomous cross-subject context learning for internet-of-things applications
• Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges
• Sequential Behavior Prediction Based on Hybrid Similarity and Cross-User Activity Transfer
• Using Smart Homes to Detect and Analyze Health Events
• Introduction to Transfer Learning
• Estimating energy expenditure using body-worn accelerometers: a comparison of methods, sensors number and positioning.
• K-center Problem
• A Reliable and Reconfigurable Signal Processing Framework for Estimation of Metabolic Equivalent of Task in Wearable Sensors
• An Energy-Efficient Computational Model for Uncertainty Management in Dynamically Changing Networked Wearables
• Investigating the Accuracy of Fitbit Activity Trackers
• Hidden Markov Model Part II
• Predicting daily activities from egocentric images using deep learning.
• Stochastic methods (Simulated Annealing)
• Approximation Problems (Vertex cover and TSP)
• AdaBoost.R2 and Two-stage TrAdaBoost.R2 (Transfer Learning for regression models)
• Boosting for Regression Transfer
• Assessing Accuracy Performance of Fitbit Activity Trackers
• Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition
• Network Flow Problem
• MET Calculation using Wearable Sensors.
• Impact of sensor misplacement on estimating metabolic equivalent of task with wearables
• Met calculations from on-body accelerometers for exergaming movements.