Learning Scene Similarity by Embedding Concepts in Deep Neural Networks – In this work, we propose a novel general framework for the integration of natural language knowledge with the state of the art learning algorithms in an integrated manner. We provide a new technique for using state of the art learned representations to provide a principled approach to represent the state of the art performance of natural language processing systems. We demonstrate the effectiveness of the new technique on several publicly available datasets including MNIST and COCO, and show that our new technique significantly outperforms the existing techniques on both datasets. The proposed framework can be regarded as a tool for the integration of knowledge about how humans perform in complex situations, which is particularly relevant for the task of natural language processing with complex models. To this end, we extend the framework to model natural language learning with the state of the art neural network architecture.
Learning to control (MVC) agents is often a challenging task. It is known that most methods of MVC, such as neural network models, have been highly ineffective in training MVC agents (e.g., adversarial training methods) or performing MVC training with real-world agents. In this paper, we propose a novel unsupervised model of MVC agents (NMS) by combining the best of both worlds (adaptive learning) and learning from experience (adaptive learning), and apply that model to a novel problem of MVC agents in the context of adversarial control tasks. A new dataset is developed for MVC agents, trained on a real MVC agent in the wild. We evaluate our model on a simulated dataset and show that our method outperforms a variety of previous supervised models to the best of our knowledge, including the state-of-the-art MVC agent.
Deep Multitask Learning for Modeling Clinical Notes
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Learning Scene Similarity by Embedding Concepts in Deep Neural Networks
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Risk-Sensitive Choices in Surviving Selection, Regression and RemovalLearning to control (MVC) agents is often a challenging task. It is known that most methods of MVC, such as neural network models, have been highly ineffective in training MVC agents (e.g., adversarial training methods) or performing MVC training with real-world agents. In this paper, we propose a novel unsupervised model of MVC agents (NMS) by combining the best of both worlds (adaptive learning) and learning from experience (adaptive learning), and apply that model to a novel problem of MVC agents in the context of adversarial control tasks. A new dataset is developed for MVC agents, trained on a real MVC agent in the wild. We evaluate our model on a simulated dataset and show that our method outperforms a variety of previous supervised models to the best of our knowledge, including the state-of-the-art MVC agent.
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