Robust Multi-Task Learning on GPU Using Recurrent Neural Networks


Robust Multi-Task Learning on GPU Using Recurrent Neural Networks – Many machine learning algorithms for machine learning tasks (like a large-scale image classification problem, a question answering task, or a statistical classification problem) are computationally intensive to compute. We propose a novel machine learning approach for efficient computation: a low-level deep convolutional neural network (CNN) that maps a set of labeled and unlabeled data items to a sparse vector. The CNN learns to use input data as a regular vector matrix, which is then used to encode a lower-level structure for the labeled data. Then, a global model of the data is trained to learn to predict the labeled data vector to be used for solving the classification task. Extensive experiments on synthetic and real datasets demonstrate the effectiveness of this approach on a large classifier-based classification problem, for which the state-of-the-art learning rate is 20x. We further demonstrate that our method outperforms previous state-of-the-art CNNs.

In a nutshell, we propose a simple, yet effective method for online feature extraction in video. The main idea is to extract a set of features into the hidden variable space without using any external knowledge. We show results on both human and machine learning datasets that show that the proposed method achieves competitive predictions in a variety of video contexts, and that it outperforms state-of-the-art methods by a significant margin.

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Robust Multi-Task Learning on GPU Using Recurrent Neural Networks

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  • Inference in Markov Emissions with Gaussian Processes

    Fast Multi-scale Deep Learning for Video ClassificationIn a nutshell, we propose a simple, yet effective method for online feature extraction in video. The main idea is to extract a set of features into the hidden variable space without using any external knowledge. We show results on both human and machine learning datasets that show that the proposed method achieves competitive predictions in a variety of video contexts, and that it outperforms state-of-the-art methods by a significant margin.


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