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Di Zhao
High-accuracy optimization is the key component of time-sensitive applications in computer sciences such as machine learning, and we develop single-GPU Iterative Discrete Approximation Monte Carlo Optimization (IDA-MCS) and multi-GPU IDA-MCS in our previous research. However, because of the memory capability constrain of GPUs in a workstation, single-GPU IDA-MCS and multi-GPU IDA-MCS may be in low […]
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Tomas Ekeberg, Stefan Engblom, Jing Liu
The classical method of determining the atomic structure of complex molecules by analyzing diffraction patterns is currently undergoing drastic developments. Modern techniques for producing extremely bright and coherent X-ray lasers allow a beam of streaming particles to be intercepted and hit by an ultrashort high energy X-ray beam. Through machine learning methods the data thus […]
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Quan Zhou, Wenlin Chen, Shiji Song, Jacob R. Gardner, Kilian Q. Weinberger, Yixin Chen
The past years have witnessed many dedicated open-source projects that built and maintain implementations of Support Vector Machines (SVM), parallelized for GPU, multi-core CPUs and distributed systems. Up to this point, no comparable effort has been made to parallelize the Elastic Net, despite its popularity in many high impact applications, including genetics, neuroscience and systems […]
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D.William Albert, K.Fayaz, D.Veerabhadra Babu
Apriori-Based algorithms are widely used for association rule mining. However, these algorithms cannot exploit the parallel processing power of modern GPU (Graphics Processing Unit). To make an algorithm to be compatible with GPU, it needs to be changed in representation of data, parallel processing and also in support count. In this paper we propose an […]
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D.William Albert, Dr.K.Fayaz, D.Veerabhadra Babu
Frequent pattern mining is one of the widely used data mining techniques for discovering trends or patterns from databases. As data is growing in exponential pace, data mining activities need more powerful computing. Fortunately modern GPUs (Graphics Processing Units) have specialized electronic circuits and support parallel processing. GPUs are capable of processing huge amount of […]
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Ken Chatfield, Karen Simonyan, Andrew Zisserman
We investigate the gains in precision and speed, that can be obtained by using Convolutional Networks (ConvNets) for on-the-fly retrieval – where classifiers are learnt at run time for a textual query from downloaded images, and used to rank large image or video datasets. We make three contributions: (i) we present an evaluation of state-of-the-art […]
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Diego Marron, Albert Bifet, Gianmarco De Francisci Morales
Random Forests is a classical ensemble method used to improve the performance of single tree classifiers. It is able to obtain superior performance by increasing the diversity of the single classifiers. However, in the more challenging context of evolving data streams, the classifier has also to be adaptive and work under very strict constraints of […]
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Andrew L. Maas, Awni Y. Hannun, Christopher T. Lengerich, Peng Qi, Daniel Jurafsky, Andrew Y. Ng
Deep neural networks (DNNs) are now a central component of nearly all state-of-the-art speech recognition systems. Part of the promise of DNNs is their ability to represent increasingly complex functions as the number of DNN parameters increases. This paper investigates the performance of DNN-based hybrid speech recognition systems as DNN model size and training data […]
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Tianning Steven Han
The complex nature of visual similarity makes it extremely difficult to hand code a set of good features that incorporate all of the important aspects for all images. This thesis work shows that machine learning techniques can be used to generate statistically optimal low dimensional features that work well with calculating similarity using Euclidean distance […]
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Yang You, Shuaiwen Leon Song, Haohuan Fu, Andres Marquez, Guangwen Yang, Kevin Barker, Kirk W. Cameron, Maryam Mehri Dehnavi, Amanda Peters Randles
Support Vector Machine (SVM) has been widely used in data-mining and Big Data applications as modern commercial databases start to attach an increasing importance to the analytic capabilities. In recent years, SVM was adapted to the field of High Performance Computing for power/performance prediction, auto-tuning, and runtime scheduling. However, even at the risk of losing […]
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Ying Deng, Edward Khon, Yuanruo Liang, Terence Lim, Jun Wei Ng, Lixiaonan Yin
Gaussian process models (henceforth Gaussian Processes) provide a probabilistic, non-parametric framework for inferring posterior distributions over functions from general prior information and observed noisy function values. This, however, comes with a computational burden of O(N3) for training and O(N2) for prediction, where N is the size of the training set [1]. Therefore, this method does […]
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Dominik Grewe
Heterogeneous computer systems are ubiquitous in all areas of computing, from mobile to high-performance computing. They promise to deliver increased performance at lower energy cost than purely homogeneous, CPU-based systems. In recent years GPU-based heterogeneous systems have become increasingly popular. They combine a programmable GPU with a multi-core CPU. GPUs have become flexible enough to […]
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