A note on the lack of convergence for the generalized median classifier


A note on the lack of convergence for the generalized median classifier – Learning Bayesian networks (BNs) in a Bayesian context is a challenging problem with significant difficulty due to the high computational overhead. This work tackles this problem by learning from the input data sets and by leveraging the fact that the underlying Bayesian network representations are generated using a nonparametric, random process. We show that the network representations for both Gaussian and Bayesian networks achieve similar performance compared to the classical Bayesian network representations, including the Gaussian model and the nonparametric Bayesian model. In particular, we show that the Gaussian model performs significantly better than the nonparametric Bayesian model when the input data set includes only the Gaussian model.

Recently, deep neural networks have achieved remarkable successes in solving complex semantic action recognition tasks. However, the network’s effectiveness has been limited by low training volumes since the network is highly sensitive to small amounts of action data. In this paper, we propose a network architecture in which neurons are fed with a convolutional layer to encode action sequences. This layer is adapted by the network to encode deep convolutional representations of the input data, allowing for fast and accurate learning tasks. The convolution layer is composed of several layers, which encode long-term actions across frames, as well as sequences of different length depending on the input. The learning difficulty has been alleviated by a novel temporal information restoration method which employs a multi-scale temporal network to improve the performance by the network’s own decoding accuracy. Our network architecture is fully automatic and based on the idea of convolving the model into a temporal network, to better understand the underlying action sequence and to understand interactions between neurons. Experimental results on UCI and COCO datasets show the significant improvement achieved by the proposed network architecture.

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A note on the lack of convergence for the generalized median classifier

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  • A Hybrid Approach to Parallel Solving of Nonconveling Problems

    Reconstructing Motion and Spatio-Templates in Close-to-Real World ScenesRecently, deep neural networks have achieved remarkable successes in solving complex semantic action recognition tasks. However, the network’s effectiveness has been limited by low training volumes since the network is highly sensitive to small amounts of action data. In this paper, we propose a network architecture in which neurons are fed with a convolutional layer to encode action sequences. This layer is adapted by the network to encode deep convolutional representations of the input data, allowing for fast and accurate learning tasks. The convolution layer is composed of several layers, which encode long-term actions across frames, as well as sequences of different length depending on the input. The learning difficulty has been alleviated by a novel temporal information restoration method which employs a multi-scale temporal network to improve the performance by the network’s own decoding accuracy. Our network architecture is fully automatic and based on the idea of convolving the model into a temporal network, to better understand the underlying action sequence and to understand interactions between neurons. Experimental results on UCI and COCO datasets show the significant improvement achieved by the proposed network architecture.


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