Hierarchical Learning for Distributed Multilabel Learning


Hierarchical Learning for Distributed Multilabel Learning – The main feature of neural networks is the use of a multilabel feature representation where the number of hidden variables in the feature space is much higher than the number of feature words that are available for each class. To address this, we construct the multilabel feature representation using hierarchical recurrent neural networks (HSRN). HSRN is a deep recurrent neural network (RNN), which first learns an RNN and evaluates its parameters at each step. Then, our network is trained in an RNN to evaluate the parameters and learns an RNN to evaluate the weights of the RNN. Our multi-layer feedforward neural network (MLN) model achieves state-of-the-art performance on the MNIST dataset.

Training Convolutional Neural Networks (CNNs) on large-scale, unlabeled data was considered a key challenge due to the difficulty in training discriminative models. In this paper, we provide a generalization of the standard CNN approach of inferring labels from unlabeled data. We propose a novel technique for a non-convex optimization problem where the objective is to optimize the training data by solving a discrete, non-convex, problem. Our approach shows promising theoretical results.

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Hierarchical Learning for Distributed Multilabel Learning

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  • Hierarchical Learning for Distributed Multilabel Learning

    An Expectation-Propagation Based Approach for Transfer Learning of Reinforcement Learning AgentsTraining Convolutional Neural Networks (CNNs) on large-scale, unlabeled data was considered a key challenge due to the difficulty in training discriminative models. In this paper, we provide a generalization of the standard CNN approach of inferring labels from unlabeled data. We propose a novel technique for a non-convex optimization problem where the objective is to optimize the training data by solving a discrete, non-convex, problem. Our approach shows promising theoretical results.


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