Search Results for author:
Found 8 papers, 3 papers with code
Multimodal Pretraining of Medical Time Series and Notes
In downstream tasks, including in-hospital mortality prediction and phenotyping, our pretrained model outperforms baselines in settings where only a fraction of the data is labeled, emphasizing its ability to enhance ICU data analysis.
DynImp: Dynamic Imputation for Wearable Sensing Data Through Sensory and Temporal Relatedness
We argue that traditional methods have rarely made use of both times-series dynamics of the data as well as the relatedness of the features from different sensors.
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition
At its core is an implicit variational distribution on binary gates that are dependent on previous observations, which will select the next subset of features to observe.
Growing Representation Learning
However, many of these methods do not have a detection method for new classes or make assumptions about the distribution of classes.
Self-Damaging Contrastive Learning
Hence, the key innovation in SDCLR is to create a dynamic self-competitor model to contrast with the target model, which is a pruned version of the latter.
Learning to Generate Clinically Coherent Chest X-Ray Reports
Automated radiology report generation has the potential to reduce the time clinicians spend manually reviewing radiographs and streamline clinical care.
Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery
Activity recognition in wearable computing faces two key challenges: i) activity characteristics may be context-dependent and change under different contexts or situations; ii) unknown contexts and activities may occur from time to time, requiring flexibility and adaptability of the algorithm.
Adaptive Activity Monitoring with Uncertainty Quantification in Switching Gaussian Process Models
This power-efficient sensing scheme can be achieved by deciding which group of sensors to use at a given time, requiring an accurate characterization of the trade-off between sensor energy usage and the uncertainty in ignoring certain sensor signals while monitoring.
