Risk-Sensitive Choices in Surviving Selection, Regression and Removal


Risk-Sensitive Choices in Surviving Selection, Regression and Removal – 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.

The present paper describes an algorithm based on convolutional networks trained for spatial pattern classification called MapReduce. This method makes use of the deep network architecture of both a model trained on the same spatial pattern and the data itself. This allows the model to learn effectively from the data without having access to the raw spatial patterns. MapReduce model learns the spatial patterns by solving a neural model of the data. The map is used to learn the model’s latent representation. The latent representation of the spatial pattern is then used to generate the data. The network achieves good performance over existing methods when trained on the real world data.

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Risk-Sensitive Choices in Surviving Selection, Regression and Removal

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    Unsupervised learning of spatial patterns by nonlinear denoising autoencodersThe present paper describes an algorithm based on convolutional networks trained for spatial pattern classification called MapReduce. This method makes use of the deep network architecture of both a model trained on the same spatial pattern and the data itself. This allows the model to learn effectively from the data without having access to the raw spatial patterns. MapReduce model learns the spatial patterns by solving a neural model of the data. The map is used to learn the model’s latent representation. The latent representation of the spatial pattern is then used to generate the data. The network achieves good performance over existing methods when trained on the real world data.


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