Deep learning for the classification of emotionally charged events

Deep learning for the classification of emotionally charged events – Recent studies have shown that the ability of deep learning to generalize to complex neural networks (NNs) of complex structures is critical to achieve large-scale classification accuracies. In this work we propose a novel deep neural network based approach that simultaneously learns from complex networks and performs action recognition based on a large number of state-of-the-art multi-task learning methods. To our knowledge this is the first attempt at generalizing action recognition from networks, given a complex-structural model, and directly performing action recognition using the complex object representation representation. Our experiments on two real-world datasets show that the proposed method achieves significant improvements in both accuracies and generalization performance over the state-of-the-art models when compared to state-of-the-art methods in the visual recognition class. Our experiments also show that the proposed deep network architecture is highly effective for learning rich visual recognition models.

We present the first real-time and scalable approach for the problem of predicting the outcome of a simulated election. The prediction is performed during the course of a daily election. One particular feature of this challenge is that even for a few days, the forecast accuracy of the prediction is always significantly lower than that of the observed forecast.

We consider the problem of extracting salient and unlabeled visual features from a text, and thus derive an approach that works well for image classification. As the task is multi-dimensional, we design a deep Convolutional Neural Network (CNN) that learns a visual feature descriptor by minimizing an image’s weighting process using the sum of the global feature maps, then learning a descriptor from the weights in the final feature map. We illustrate the effectiveness of CNNs trained on the Penn Treebank (TT) dataset and a standard benchmark task for image classification.

Sparse Representation by Partial Matching

Supervised Feature Selection Using Graph Convolutional Neural Networks

Deep learning for the classification of emotionally charged events

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  • An Empirical Evaluation of Unsupervised Learning Methods based on Hidden Markov Models

    Predicting Daily Activity with a Deep Neural NetworkWe present the first real-time and scalable approach for the problem of predicting the outcome of a simulated election. The prediction is performed during the course of a daily election. One particular feature of this challenge is that even for a few days, the forecast accuracy of the prediction is always significantly lower than that of the observed forecast.

    We consider the problem of extracting salient and unlabeled visual features from a text, and thus derive an approach that works well for image classification. As the task is multi-dimensional, we design a deep Convolutional Neural Network (CNN) that learns a visual feature descriptor by minimizing an image’s weighting process using the sum of the global feature maps, then learning a descriptor from the weights in the final feature map. We illustrate the effectiveness of CNNs trained on the Penn Treebank (TT) dataset and a standard benchmark task for image classification.


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