Search Results for author:
Found 7 papers, 3 papers with code
Multi-Task Learning for Post-transplant Cause of Death Analysis: A Case Study on Liver Transplant
Organ transplant is the essential treatment method for some end-stage diseases, such as liver failure.
Towards Fair Patient-Trial Matching via Patient-Criterion Level Fairness Constraint
To tackle these challenges, deep learning frameworks have been created to match patients to trials.
PheME: A deep ensemble framework for improving phenotype prediction from multi-modal data
In this work, we present PheME, an Ensemble framework using Multi-modality data of structured EHRs and unstructured clinical notes for accurate Phenotype prediction.
Fairly Predicting Graft Failure in Liver Transplant for Organ Assigning
To tackle this problem, this work proposes a fair machine learning framework targeting graft failure prediction in liver transplant.
Towards Automated Imbalanced Learning with Deep Hierarchical Reinforcement Learning
Motivated by this, we investigate developing a learning-based over-sampling algorithm to optimize the classification performance, which is a challenging task because of the huge and hierarchical decision space.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
AutoVideo: An Automated Video Action Recognition System
Action recognition is an important task for video understanding with broad applications.
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.
