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Found 5 papers, 1 papers with code
An Improved Strategy for Blood Glucose Control Using Multi-Step Deep Reinforcement Learning
Our work validates the effectiveness of multi-step reinforcement learning in BG control, which may help to explore the optimal glycemic control measure and improve the survival of diabetic patients.
Interactive Visual Task Learning for Robots
With both of these results we demonstrate the ability of our model to learn tasks and concepts in a continual learning setting on the robot.
An Empirical Study on Finding Spans
We present an empirical study on methods for span finding, the selection of consecutive tokens in text for some downstream tasks.
Iterative Document-level Information Extraction via Imitation Learning
We present a novel iterative extraction model, IterX, for extracting complex relations, or templates (i. e., N-tuples representing a mapping from named slots to spans of text) within a document.
Link Prediction via Graph Attention Network
Link prediction aims to infer missing links or predicting the future ones based on currently observed partial networks, it is a fundamental problem in network science with tremendous real-world applications.
