Dictionary Learning with Conditional Random Fields

Dictionary Learning with Conditional Random Fields – We present a novel approach for learning from data using probabilistic model learning (PML). The model-based training procedure is based on probabilistic assumptions on the underlying knowledge graph and the output of the PML algorithm is constrained by the knowledge graph. In PML, the learned knowledge graph is a representation of the knowledge graph of a probabilistic model and the output is a function of the underlying data. Using the input data and PML’s conditional independence measure on the underlying graph, we can estimate the posterior of the PML algorithm by learning the model parameters. Experiments conducted on two real world datasets and the resulting inference procedure has shown that the proposed method is superior to its counterpart, the probabilistic framework.

Deep reinforcement learning (DRL) has been successfully applied to the task of predicting the health of a human being. In this article, a DRL approach is proposed to perform reinforcement learning (RL). The learning objective for a RL system consists of finding optimal strategies for a given task, and is formulated as a multi-task learning problem. This can be represented by a set of reinforcement learning algorithms, and can be solved by different reinforcement learning algorithms that learn to minimize the variance in the output of the RL algorithm. Several different reinforcement learning algorithms are used for learning to model the current state of the RL. The RL algorithms of this work are implemented in the framework of a two-stage neural network architecture (NN), in which the RL algorithm is modified via learning to learn new policies. Experimental results conducted on a real-world dataset, with a number of simulated instances, illustrate the superior generalization performance of the proposed RL-SRNN architecture compared to the traditional RL algorithms.

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Dictionary Learning with Conditional Random Fields

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  • Mixture of DAG-causal patterns and conditional probability in trainable Bayesian networks

    A Unified Deep Architecture for Structured PredictionDeep reinforcement learning (DRL) has been successfully applied to the task of predicting the health of a human being. In this article, a DRL approach is proposed to perform reinforcement learning (RL). The learning objective for a RL system consists of finding optimal strategies for a given task, and is formulated as a multi-task learning problem. This can be represented by a set of reinforcement learning algorithms, and can be solved by different reinforcement learning algorithms that learn to minimize the variance in the output of the RL algorithm. Several different reinforcement learning algorithms are used for learning to model the current state of the RL. The RL algorithms of this work are implemented in the framework of a two-stage neural network architecture (NN), in which the RL algorithm is modified via learning to learn new policies. Experimental results conducted on a real-world dataset, with a number of simulated instances, illustrate the superior generalization performance of the proposed RL-SRNN architecture compared to the traditional RL algorithms.


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