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Deep Learning Training Procedure Augmentations
The results show the effectiveness of the technique for improving training time as well as offer some new insight into the training process of deep networks.
Dynamic Batch Adaptation
Additionally, we find that DBA produces an increased improvement over standard optimizers when used in data scarce conditions where, in addition to convergence speed, it also significantly improves model generalization, managing to train a network with a single fully connected hidden layer using only 1% of the MNIST dataset to reach 97. 79% test accuracy.
UAIC at SemEval-2019 Task 3: Extracting Much from Little
In this paper, we present a system description for implementing a sentiment analysis agent capable of interpreting the state of an interlocutor engaged in short three message conversations.
