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Found 6 papers, 0 papers with code
Gradient-flow adaptive importance sampling for Bayesian leave one out cross-validation for sigmoidal classification models
We introduce a set of gradient-flow-guided adaptive importance sampling (IS) transformations to stabilize Monte-Carlo approximations of point-wise leave one out cross-validated (LOO) predictions for Bayesian classification models.
Distilling Multi-Scale Knowledge for Event Temporal Relation Extraction
Event Temporal Relation Extraction (ETRE) is paramount but challenging.
Self-supervised Representation Learning on Electronic Health Records with Graph Kernel Infomax
We propose Graph Kernel Infomax, a self-supervised graph kernel learning approach on the graphical representation of EHR, to overcome the previous problems.
The Analysis from Nonlinear Distance Metric to Kernel-based Drug Prescription Prediction System
Distance metrics and their nonlinear variant play a crucial role in machine learning based real-world problem solving.
Cross-Global Attention Graph Kernel Network Prediction of Drug Prescription
We present an end-to-end, interpretable, deep-learning architecture to learn a graph kernel that predicts the outcome of chronic disease drug prescription.
GUIR at SemEval-2020 Task 12: Domain-Tuned Contextualized Models for Offensive Language Detection
Offensive language detection is an important and challenging task in natural language processing.
