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Found 4 papers, 0 papers with code
SurGNN: Explainable visual scene understanding and assessment of surgical skill using graph neural networks
By using GNNs to analyze the complex visual data of surgical procedures represented as graph structures, relevant features can be extracted and surgical skill can be predicted.
RefiNeRF: Modelling dynamic neural radiance fields with inconsistent or missing camera parameters
We demonstrate the effectiveness of our method on a variety of static and dynamic scenes and show that it outperforms traditional SfM and MVS approaches.
wildNeRF: Complete view synthesis of in-the-wild dynamic scenes captured using sparse monocular data
We present a novel neural radiance model that is trainable in a self-supervised manner for novel-view synthesis of dynamic unstructured scenes.
Advances in Prediction of Readmission Rates Using Long Term Short Term Memory Networks on Healthcare Insurance Data
We developed a bi-directional Long Short Term Memory (LSTM) Network that is able to use readily available insurance data (inpatient visits, outpatient visits, and drug prescriptions) to predict 30 day re-admission for any admitted patient, regardless of reason.
