MorphNet: A Python-based Entity Disambiguation Toolkit – We show how deep neural networks can be used as a semi-supervised visual recognition system to predict human action. Most supervised approaches to human action prediction from deep neural networks tend to be based on hand-crafted features. We demonstrate how to use these features for the task of face recognition from a deep model, namely learning to predict the action that is likely to be seen by a robot’s visual system. We show that the human model is able to be used as a semi-supervised visualization tool to predict human actions using only hand-crafted features and the human face as a single node. We compare our results to the state-of-the-art models on both synthetic and real data, and show that our model outperforms them.
We propose a new algorithm to solve the inference problem that considers the knowledge about the meaning of given words. This algorithm has an advantage that it gives a good solution for all the problems that have been encountered in prior studies such as the Bayesian and the Bayesian network. We demonstrate that the proposed algorithm achieves the proposed result in a general setting with a very limited number of predictions.
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MorphNet: A Python-based Entity Disambiguation Toolkit
Learning Graph Embedding from Text: The Power of Text ExplanationsWe propose a new algorithm to solve the inference problem that considers the knowledge about the meaning of given words. This algorithm has an advantage that it gives a good solution for all the problems that have been encountered in prior studies such as the Bayesian and the Bayesian network. We demonstrate that the proposed algorithm achieves the proposed result in a general setting with a very limited number of predictions.