The Statistical Analysis Unit for Random Forests


The Statistical Analysis Unit for Random Forests – We evaluate the effectiveness of a novel deep learning (DNN) architecture, called Deep Network-Aware, on predicting the next $N$ steps from a random forest, without using a pre-trained model. We show that the underlying strategy of our DNN works well: it effectively predicts the next $N$ steps, by minimizing the risk and the uncertainty. It is also consistent with our earlier work that the loss of the network for $N$ moves from the $N$ to the next step.

This paper proposes a novel method for real-time brain network prediction, via a learning-to-learn paradigm. A supervised learning framework is then developed to perform multi-task, multi-context learning. To handle the need to model long-term dependencies, we propose an iterative update of the neural network, which in turn leverages local recurrent connections to learn to predict long-term connections. The iterative framework, which we call a recurrent-bisterent framework, uses the output of a pair-wise graph to predict the most relevant local connections based on their correlation and is robust to variations in parameters. Moreover, by using the prediction results from this framework, a training set of long-term brain connections is obtained. The proposed method is evaluated on several benchmark data sets, showing that our method has high predictive performance and provides good computational power.

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The Statistical Analysis Unit for Random Forests

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    EgoModeling: Real-time Modelling of Brain ConnectionsThis paper proposes a novel method for real-time brain network prediction, via a learning-to-learn paradigm. A supervised learning framework is then developed to perform multi-task, multi-context learning. To handle the need to model long-term dependencies, we propose an iterative update of the neural network, which in turn leverages local recurrent connections to learn to predict long-term connections. The iterative framework, which we call a recurrent-bisterent framework, uses the output of a pair-wise graph to predict the most relevant local connections based on their correlation and is robust to variations in parameters. Moreover, by using the prediction results from this framework, a training set of long-term brain connections is obtained. The proposed method is evaluated on several benchmark data sets, showing that our method has high predictive performance and provides good computational power.


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