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Konu: Artificial intelligence.
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by
Franklin, Bryan M., author.
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. Finally, the data collected was used to train a set of models to predict which type of parallel learner
by
Ma, Min, author. (orcid)0000-0002-1816-1772
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parameters while fine-tuning the output layer in order to adapt neural network language models (NNLMs) from
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Lu, Junwei, author.
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graphical models. We propose a novel class of dynamic nonparanormal graphical models, which allows us to
by
Izenov, Yesdaulet, author.
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prediction task without ever storing input samples are the key concepts of streaming machine learning models
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Flanagan, Brian, author.
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predicted using a combination of decision forests and hidden Markov models, using vehicle speed and steering
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Kokate, Apurva, author. (orcid)0000-0003-2353-4171
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behavior of deep models such as convolutional neural networks (CNNs) and provide visual interpretations of
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Toor, Andeep Singh, author.
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-text embedding, recurrence and sequencing, and memory models to interpret the queries and best answer them.
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Qassim, Hussam, author.
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convergence, and degradation. The proposed models came in two models (Residual-CNDS8), and (Residual-CNDS10
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Ly, Phillip, author.
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. Furthermore, the proposed deep learning methodologies generate mobile compatible models by rendering and
by
Gundala palle, Santosh reddy, author.
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performance for our application models by satisfying the quality of service level agreements (QOSLA). By using
by
Cai, Ermao, author.
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systems. For technology-aware multi-core system design, we learn accurate performance and power models for
by
Qiu, Liang, author.
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back end, models or methods such as Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), Bags
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