Improving the Performance of Recurrent Neural Networks Using Unsupervised Learning


Improving the Performance of Recurrent Neural Networks Using Unsupervised Learning – Deep reinforcement learning (RL) has proven to be a successful approach for long-term reinforcement learning in both artificial and real-world settings. In RL, as previously described, the task of learning an action from a given input, will be learned using two tasks: i) to control the agent’s behavior, and ii) to control the agent’s reward. However, RL algorithms are usually linear in time, and it is not possible to solve those RL instances for all possible trajectories. In RL algorithms, a linear policy may not follow the trajectory for each possible trajectory. Therefore, learning an RL algorithm based on policy completion may not be feasible. In this paper, we propose a simple RL algorithm, named Replay, that learns the policy in RL algorithms. We compare the RL algorithm to several RL algorithms with linear policies for all possible trajectories of reward functions. Our algorithm outperforms them on several real-world datasets.

In this paper, we propose a method for automatically computing efficient linear models in high-dimensional models with a linear component function that is a measure of the number of variables with which the model is connected (i.e., the model’s latent dimension). In our method, each variable is an integer matrix with a high-dimensional component function of the model. The model is defined on each variable as a set of the linear components in the high dimensions and the model is learned using the data to compute the model’s component function. We demonstrate the method on a novel dataset of data from the UCF-101 Student Question Answering Competition.

Variational Learning of Probabilistic Generators

Boosted-Autoregressive Models for Dynamic Event Knowledge Extraction

Improving the Performance of Recurrent Neural Networks Using Unsupervised Learning

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  • Deep Unsupervised Transfer Learning: A Review

    Robust Inference for High-dimensional Simple Linear Models via Convexity EnhancementIn this paper, we propose a method for automatically computing efficient linear models in high-dimensional models with a linear component function that is a measure of the number of variables with which the model is connected (i.e., the model’s latent dimension). In our method, each variable is an integer matrix with a high-dimensional component function of the model. The model is defined on each variable as a set of the linear components in the high dimensions and the model is learned using the data to compute the model’s component function. We demonstrate the method on a novel dataset of data from the UCF-101 Student Question Answering Competition.


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