Social sciences
Working Digital Money into a Cash Economy
Deep Learning Approach for Extreme Multi-label Text Classification
Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document…
EZLearn: Exploiting Organic Supervision in Large-Scale Data Annotation
Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document…
Precision-Recall versus Accuracy and the Role of Large Data Sets
Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document…
A Reduction Principle for Generalizing Bona Fide Risk Bounds in Multi-class Setting
Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document…
Extreme Classification in Healthcare
Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document…
Extreme Multi-label Learning via Nearest Neighbor Graph Partitioning and Embedding
Extreme classification is a rapidly growing research area focusing on multi-class and multi-label problems involving an extremely large number of labels. Many applications have been found in diverse areas ranging from language modeling to document…
