14303
Clemens-Alexander Brust, Sven Sickert, Marcel Simon, Erik Rodner, Joachim Denzler
In this paper, we present convolutional patch networks, which are convolutional (neural) networks (CNN) learned to distinguish different image patches and which can be used for pixel-wise labeling. We show how to easily learn spatial priors for certain categories jointly with their appearance. Experiments for urban scene understanding demonstrate state-of-the-art results on the LabelMeFacade dataset. […]
Baoguang Shi, Xiang Bai, Cong Yao
Image-based sequence recognition has been a long-standing research topic in computer vision. In this paper, we investigate the problem of scene text recognition, which is among the most important and challenging tasks in image-based sequence recognition. A novel neural network architecture, which integrates feature extraction, sequence modeling and transcription into a unified framework, is proposed. […]
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Keiron O'Shea
Greedy Restrictive Boltzmann Machines yield an fairly low 0.72% error rate on the famous MNIST database of handwritten digits. All that was required to achieve this result was a high number of hidden layers consisting of many neurons, and a graphics card to greatly speed up the rate of learning.
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Carlo D'Eramo
The Deep Boltzmann Machine (DBM) has been proved to be one of the most effective machine learning generative models in discriminative tasks. They’ve been able to overcome other generative, and even discriminative models, on relatively simple tasks, such as digits recognition, and also on more complex tasks such as simple objects recognition. However, there’re only […]
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Murali Krishna Emani
Modern day hardware platforms are parallel and diverse, ranging from mobiles to data centers. Mainstream parallel applications execute in the same system competing for resources. This resource contention may lead to a drastic degradation in a program’s performance. In addition, the execution environment composed of workloads and hardware resources, is dynamic and unpredictable. Efficient matching […]
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Limin Wang, Yuanjun Xiong, Zhe Wang, Yu Qiao
Deep convolutional networks have achieved great success for object recognition in still images. However, for action recognition in videos, the improvement of deep convolutional networks is not so evident. We argue that there are two reasons that could probably explain this result. First the current network architectures (e.g. Two-stream ConvNets) are relatively shallow compared with […]
Anton Akusok, Kaj-Mikael Bjork, Yoan Miche, Amaury Lendasse
This work presents a complete approach to a successful utilization of a high performance Extreme Learning Machines (ELMs) Toolbox for Big Data. It summarizes recent advantages in algorithmic performance; gives a fresh view on the ELM solution in relation to the traditional linear algebraic performance; and reaps the latest software and hardware performance achievements. The […]
Genlang Chen, Chenggang Lai, Miaoqing Huang
Sparse coding has been a popular learning model in machine learning field. However, due to the complexity of the learning model, the high computational cost has seriously hindered its application. Toward this purpose, this paper presents a parallel sparse coding method to improve the performance by exploiting the power of acceleration technologies such as Intel […]
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Shiyu Chang, Wei Han, Jiliang Tang, Guo-Jun Qi, Charu C. Aggarwal, Thomas S. Huang
Data embedding is used in many machine learning applications to create low-dimensional feature representations, which preserves the structure of data points in their original space. In this paper, we examine the scenario of a heterogeneous network with nodes and content of various types. Such networks are notoriously difficult to mine because of the bewildering combination […]
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Karthik Narayan, Ali Punjani, Pieter Abbeel
Although recent work in non-linear dimensionality reduction investigates multiple choices of divergence measure during optimization (Yang et al., 2013; Bunte et al., 2012), little work discusses the direct effects that divergence measures have on visualization. We study this relationship, theoretically and through an empirical analysis over 10 datasets. Our works shows how the alpha and […]
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Thomas Nelson, Axel Rivera, Prasanna Balaprakash, Mary Hall, Paul D. Hovland, Elizabeth Jessup, Boyana Norris
Many scientific and numerical applications, including quantum chemistry modeling and fluid dynamics simulation, require tensor product and tensor contraction evaluation. Tensor computations are characterized by arrays with numerous dimensions, inherent parallelism, moderate data reuse and many degrees of freedom in the order in which to perform the computation. The best-performing implementation is heavily dependent on […]
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Naser Sedaghati, Te Mu, Louis-Noel Pouchet, Srinivasan Parthasarathy, P. Sadayappan
Sparse matrix-vector multiplication (SpMV) is a core kernel in numerous applications, ranging from physics simulation and large-scale solvers to data analytics. Many GPU implementations of SpMV have been proposed, targeting several sparse representations and aiming at maximizing overall performance. No single sparse matrix representation is uniformly superior, and the best performing representation varies for sparse […]
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