Deep Unsupervised Transfer Learning: A Review


Deep Unsupervised Transfer Learning: A Review – We address the task of unsupervised transfer learning for an unknown action whose action has not been labeled by its target agent. We exploit the ability of the learning agent to predict that a given action has been labeled by a target agent (e.g., a toy movie with a strong strong action). We model an action as a sequence of actions (i.e., action classes) that are labeled by another agent and our goal in unsupervised transfer learning is to make a prediction of the underlying action that is not labeled (i.e., the class of the action). We propose a novel unsupervised training scheme that is able to learn an action’s labels without a preprocessing step, thereby increasing the performance of unsupervised transfer learning.

We present a novel method for understanding temporal ambiguity in the wild. The proposed model is a neural network trained to predict the current tense state of a language user’s speech, or a sequence of sentences. As the user’s speech becomes more and more important (i.e., more relevant to the current tense state), this is an opportunity for the user to improve his or her understanding of the language’s tense state. An automatic learning tool, we call Temporal Context Knowledge (TCK), is used to predict the next tense state of a user’s speech to achieve a more detailed understanding of the current tense state. Our model combines the temporal context knowledge from the user and the semantic content in his or her speech into the state-action tree. We build an automatic and robust neural network model to predict the current tense state of user’s speech using the knowledge extracted by our neural network. Experiments are conducted using the MIMI dataset and on two different languages. Results show that our model outperforms current state-action learning methods for predicting the current tense state of users by a large margin.

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Deep Unsupervised Transfer Learning: A Review

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  • Automated Evaluation of Neural Networks for Polish Machine-Patch Recognition

    Exploring Temporal Context Knowledge for Real-time, Multi-lingual Conversational SearchWe present a novel method for understanding temporal ambiguity in the wild. The proposed model is a neural network trained to predict the current tense state of a language user’s speech, or a sequence of sentences. As the user’s speech becomes more and more important (i.e., more relevant to the current tense state), this is an opportunity for the user to improve his or her understanding of the language’s tense state. An automatic learning tool, we call Temporal Context Knowledge (TCK), is used to predict the next tense state of a user’s speech to achieve a more detailed understanding of the current tense state. Our model combines the temporal context knowledge from the user and the semantic content in his or her speech into the state-action tree. We build an automatic and robust neural network model to predict the current tense state of user’s speech using the knowledge extracted by our neural network. Experiments are conducted using the MIMI dataset and on two different languages. Results show that our model outperforms current state-action learning methods for predicting the current tense state of users by a large margin.


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