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SetConv: A New Approach for Learning from Imbalanced Data
For many real-world classification problems, e. g., sentiment classification, most existing machine learning methods are biased towards the majority class when the Imbalance Ratio (IR) is high.
Towards Self-Adaptive Metric Learning On the Fly
In this paper, we present a new online metric learning framework that attempts to tackle the challenge by learning an ANN-based metric with adaptive model complexity from a stream of constraints.
Deep Learning Approaches for Extracting Adverse Events and Indications of Dietary Supplements from Clinical Text
The best performed NER and RE models were further applied on clinical notes mentioning 88 DS for discovering DS adverse events and indications, which were compared with a DS knowledge base.
RLCFR: Minimize Counterfactual Regret by Deep Reinforcement Learning
Extensive experimental results on various games have shown that the generalization ability of our method is significantly improved compared with existing state-of-the-art methods.
Deep Learning on Knowledge Graph for Recommender System: A Survey
Recent advances in research have demonstrated the effectiveness of knowledge graphs (KG) in providing valuable external knowledge to improve recommendation systems (RS).
