By ensuring adequate functional coverage, End-to-End (E2E) testing is a key enabling factor of continuous integration. This is even more true for web applications, where automated E2E testing is the only way to exerci...
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In this work we present a residual based a posteriori error estimation for a heat equation with a random forcing term and a random diffusion coefficient which is assumed to depend affinely on a finite number of indepe...
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This paper is concerned with near-optimal approximation of a given function f ∈ L2([0, 1]) with elements of a polynomially enriched wavelet frame, a so-called quarklet frame. Inspired by hp-approximation techniques o...
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In mobile edge computing (MEC), the task offloading of terminals has become a hot research spot. For terminals, how to effectively utilize limited computing resources and ensure quality of service (QoS) is a key issue...
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Since conventional background normalization approaches are sensitive to the manual setting parameters such as the length of normalization window and normalization threshold, an adaptive algorithm is proposed without a...
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We consider the problem of identifying the best arm in stochastic Multi-Armed Bandits (MABs) using a fixed sampling budget. Characterizing the minimal instance-specific error probability for this problem constitutes o...
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We consider a generalization of group testing where the potentially contaminated sets are the members of a given hypergraph F = (V, E). This generalization finds application in contexts where contaminations can be con...
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In this paper, we provide a general framework for studying multi-agent online learning problems in the presence of delays and asynchronicities. Specifically, we propose and analyze a class of adaptive dual averaging s...
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In this paper, we provide a general framework for studying multi-agent online learning problems in the presence of delays and asynchronicities. Specifically, we propose and analyze a class of adaptive dual averaging schemes in which agents only need to accumulate gradient feedback received from the whole system, without requiring any between-agent coordination. In the single-agent case, the adaptivity of the proposed method allows us to extend a range of existing results to problems with potentially unbounded delays between playing an action and receiving the corresponding feedback. In the multi-agent case, the situation is significantly more complicated because agents may not have access to a global clock to use as a reference point; to overcome this, we focus on the information that is available for producing each prediction rather than the actual delay associated with each feedback. This allows us to derive adaptive learning strategies with optimal regret bounds, even in a fully decentralized, asynchronous environment. Finally, we also analyze an "optimistic" variant of the proposed algorithm which is capable of exploiting the predictability of problems with a slower variation and leads to improved regret bounds.
Current mainstream deep learning optimization algorithms fall into two categories: non-adaptive optimization algorithms like SGDM and adaptive optimization algorithms like Adam. For many deep neural network models, us...
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Multi-channel SAR system improves the suppression performance of the main lobe clutter by increasing the degree of spatial freedom. In actual project, channel mismatch will greatly reduce the performance of clutter su...
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