Search Results for author:
Found 9 papers, 5 papers with code
Domain Adaptation Under Behavioral and Temporal Shifts for Natural Time Series Mobile Activity Recognition
Because of the large variations present in human behavior, we collect data from many participants across two different age groups.
Mimic: An adaptive algorithm for multivariate time series classification
Time series data are valuable but are often inscrutable.
CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning
CALDA synergistically combines the principles of contrastive learning and adversarial learning to robustly support multi-source UDA (MS-UDA) for time series data.
Transfer Learning for Activity Recognition in Mobile Health
While activity recognition from inertial sensors holds potential for mobile health, differences in sensing platforms and user movement patterns cause performance degradation.
Multi-Source Deep Domain Adaptation with Weak Supervision for Time-Series Sensor Data
First, we propose a novel Convolutional deep Domain Adaptation model for Time Series data (CoDATS) that significantly improves accuracy and training time over state-of-the-art DA strategies on real-world sensor data benchmarks.
A Survey of Techniques All Classifiers Can Learn from Deep Networks: Models, Optimizations, and Regularization
We then survey non-neural network learning algorithms that make innovative use of these methods to improve classification.
Multi-Purposing Domain Adaptation Discriminators for Pseudo Labeling Confidence
Often domain adaptation is performed using a discriminator (domain classifier) to learn domain-invariant feature representations so that a classifier trained on labeled source data will generalize well to unlabeled target data.
Activity2Vec: Learning ADL Embeddings from Sensor Data with a Sequence-to-Sequence Model
The widespread availability of sensors implanted in homes, smartphones, and smart watches have engendered collection of big datasets that reflect human behavior.
A Survey of Unsupervised Deep Domain Adaptation
Deep learning has produced state-of-the-art results for a variety of tasks.
