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Deep Learning Hyper-parameter Tuning for Sentiment Analysis in Twitter based on Evolutionary Algorithms
This work proposes the use of the evolutionary algorithm SHADE for the optimisation of the configuration of a deep learning model for the task of sentiment analysis in Twitter, and shows that the hyper-parameters found by the evolutionary algorithms enhance the performance of the deep learning method.
Dynamic Federated Learning Model for Identifying Adversarial Clients
A dynamic federated learning model is proposed that dynamically discards those adversarial clients, which allows to prevent the corruption of the global learning model.
SCI2S at TASS 2018: Emotion Classification with Recurrent Neural Networks
The participation of the team SCI2S in all the Subtasks of the Task 4 of TASS 2018 is described and three Deep Learning models that are based on a sequence encoding layer built on a Long Short-Term Memory gated-architecture of Recurrent Neural Network are proposed.