Proceedings of the 38th Annual Workshop of the Austrian Machine Learning Association (ÖDAI), 2013


Proceedings of the 38th Annual Workshop of the Austrian Machine Learning Association (ÖDAI), 2013 – This paper presents a new tool to analyze and evaluate the performance of the state-of-the-art deep neural networks (DNNs). In fact, the traditional method of the state-of-the-art DNNs is to design a model on the data manifold without analyzing the output of the model, thus violating the model’s performance. We propose a deep neural networks (DNN) architecture that utilizes a deep convolutional network without exploiting the deep state representation. To achieve a more accurate model and less computational cost, we propose a first-order, deep learning-based framework for DNN analysis. The architecture is based on an efficient linear transformation, which is used in an ensemble model to perform the analysis. Compared with other state-of-the-art deep neural networks, our method is not necessarily faster and requires less computation.

We propose a new framework for the purpose of image annotation using multinomial random processes (NNPs). NNs encode the information contained in a set of image samples and the data are modelled as either the image samples and their distributions, or the images. In this framework, we treat the data from different samples as the same. NNs are built from multiple distributions and these are represented as a set of random Gaussian processes (GRPs). This allows the proposed framework to cope with multi-view learning problems. In this paper, the proposed framework is compared with an existing framework on two problems: the classification of image-level shapes and the classification of texture features. The experimental results demonstrate that the framework is robust and provides an alternative approach to image annotation.

Predicting the future behavior of non-monotonic trust relationships

A novel method for accurate generation of abductive report in police-station scenario with limited resources

Proceedings of the 38th Annual Workshop of the Austrian Machine Learning Association (ÖDAI), 2013

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  • Generating a Robust Multimodal Corpus for Robust Speech Recognition

    On the Relationship Between Color and Texture Features and Their Use in Shape ClassificationWe propose a new framework for the purpose of image annotation using multinomial random processes (NNPs). NNs encode the information contained in a set of image samples and the data are modelled as either the image samples and their distributions, or the images. In this framework, we treat the data from different samples as the same. NNs are built from multiple distributions and these are represented as a set of random Gaussian processes (GRPs). This allows the proposed framework to cope with multi-view learning problems. In this paper, the proposed framework is compared with an existing framework on two problems: the classification of image-level shapes and the classification of texture features. The experimental results demonstrate that the framework is robust and provides an alternative approach to image annotation.


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