3159 résultats — page 20 de 158

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One of the major challenges in large-scale distributed machine learning involving stochastic gradient methods is the high cost of gradient communication ove...

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Pruning methods for deep neural networks based on weight magnitude have shown promise in recent research. We propose a new, highly flexible approach to neura...

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The past few years have seen the ability of cooperative Malware Detection Systems (MDS) to detect complex and unknown malware. In a cooperative setting, an M...

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The design of compact deep neural networks is a crucial task to enable widespread adoption of deep neural networks in the real-world, particularly for edge a...

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Knowledge distillation is a technique that consists in training a student network, usually of a low capacity, to mimic the representation space and the perfo...

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In time-to-event data analysis, the main object of interest is the time elapsed between the occurrence of two ordered events, say E1,E2. Sampling fr...

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Training large-scale deep neural networks is a long, time-consuming operation, often requiring many GPUs to accelerate. In large models, the time spent loadi...

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Binary neural networks improve computationally efficiency of deep models with a large margin. However, there is still a performance gap between a successful...

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Min-max formulations have attracted great attention in the ML community due to the rise of deep generative models and adversarial methods, while understandin...

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The determinantal point process (DPP) provides a promising and attractive alternative to simple random sampling in cluster analysis or classification, for th...

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Deep neural networks usually have unnecessarily high complexities and possibly many features of low utility, especially for tasks that they are not designed ...

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We introduce a novel ensemble learning approach which combines random partitions models through Potts clustering with a non-parametric predictor such as sha...

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Convolutional Neural Networks (CNNs) are state-of-the-art in numerous computer vision tasks such as object classification and detection. However, the large ...

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Implementation of quantized neural networks on computing hardware leads to considerable speed up and memory saving. However, quantized deep networks are diff...

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In the area of hydraulic power generation, there is a great deal of interest in two interdependent domains: operation and maintenance. This interdependence...

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The popularity of drones is rapidly increasing across the different sectors of the economy. Aerial capabilities and relatively low costs make drones the perf...

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The context of this research is multiobjective optimization where conflicting objectives are present. In this work, these objectives are only available as th...

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Traditionally, the design of supply chains for humanitarian operations has been developed distinctly for the different disaster management phases, with littl...

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Cities worldwide struggle with overloaded transportation systems and their externalities, such as traffic congestion and emissions. The emerging technology o...

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Maintenance planning for hydropower plants is a crucial problem. In this paper, we evaluate the impact of the Hydropower Production Function (HPF) formulatio...

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