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Found 7 papers, 2 papers with code
Progressive Neural Compression for Adaptive Image Offloading under Timing Constraints
It is, therefore, important to develop an adaptive approach that maximizes the inference performance of ML applications under timing constraints and the resource constraints of IoT devices.
Assisting Clinical Decisions for Scarcely Available Treatment via Disentangled Latent Representation
TVAE is specifically designed to address the modeling challenges like ECMO with strong treatment selection bias and scarce treatment cases.
Learning to Rank Normalized Entropy Curves with Differentiable Window Transformation
We also introduce a novel differentiable indexing method for the proposed adaptive curve transformation, which allows gradients with respect to the discrete indices to flow freely through the curve transformation layer, enabling the learned window sizes to be updated flexibly during training.
HiPAL: A Deep Framework for Physician Burnout Prediction Using Activity Logs in Electronic Health Records
Burnout is a significant public health concern affecting nearly half of the healthcare workforce.
Objective-aware Traffic Simulation via Inverse Reinforcement Learning
Traffic simulators act as an essential component in the operating and planning of transportation systems.
Predicting Intraoperative Hypoxemia with Hybrid Inference Sequence Autoencoder Networks
We present an end-to-end model using streaming physiological time series to predict near-term risk for hypoxemia, a rare, but life-threatening condition known to cause serious patient harm during surgery.
Semi-supervised Federated Learning for Activity Recognition
In this paper, we propose an activity recognition system that uses semi-supervised federated learning, wherein clients conduct unsupervised learning on autoencoders with unlabelled local data to learn general representations, and a cloud server conducts supervised learning on an activity classifier with labelled data.
