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A Deep Reinforced Model for Abstractive Summarization Romain Paulus, Claiming XING and Richard Soccer Paulus, Cong, rs ocher Salesforce.comAbstractarXiv:1705.04304v2 cs. CL 19 May 2017Attentional,
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A deep reinforced model is a type of machine learning model that combines deep learning techniques with reinforcement learning to make decisions and learn from data.
Companies or individuals using deep reinforced models for decision making or analysis may be required to file them with regulatory bodies or stakeholders.
To fill out a deep reinforced model, data scientists and researchers need to set up the neural network architecture, define the reinforcement learning algorithm, and train the model with relevant data.
The purpose of a deep reinforced model is to make accurate decisions or predictions based on input data by learning the optimal behavior through trial and error.
Information such as the input data used for training, the neural network architecture, the reinforcement learning algorithm, and the model's performance metrics must be reported on a deep reinforced model.
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