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We present Net2Vec, a flexible high-performance platform that allows the execution of deep learning algorithms in the communication network.
Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
2009
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Large-scale deep unsupervised learning using graphics processors
2009
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Ibm infosphere streams for scalable, real-time, intelligent transportation services
2010
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Large-scale machine learning with stochastic gradient descent
2010
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Packetshader: a gpu-accelerated software router
2010
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Hogwild: A lock-free approach to parallelizing stochastic gradient descent
2011
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Blockmon: A high-performance composable network traffic measurement system
2012
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netmap: A novel framework for fast packet i/o
2012
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Distributed representations of words and phrases and their compositionality
2013
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Stream-monitoring with blockmon: convergence of network measurements and data analytics platforms
2013
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Wire speed name lookup: A gpu-based approach
2013
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Caffe: Convolutional architecture for fast feature embedding
2014
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Apache Kafka
Apache flink: Stream and batch processing in a single engine
2015
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I always feel like somebody’s watching me: measuring online behavioural advertising
2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
2015
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Towards a gpu sdn controller
2015
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Clipper: A low-latency online prediction serving system
2016
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User profiling in the time of https
2016
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Apache Spark Streaming
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Storm, distributed and fault-tolerant realtime computation
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CUDAMat: a CUDA-based matrix class for Python
Cited in the paper.