Results 11 to 20 of about 4,010,683 (211)
Explanations for Neural Networks by Neural Networks [PDF]
Applied Sciences, 2022 Understanding the function learned by a neural network is crucial in many domains, e.g., to detect a model’s adaption to concept drift in online learning. Existing global surrogate model approaches generate explanations by maximizing the fidelity between the neural network and a surrogate model on a sample-basis, which can be very time-consuming ...
Sascha Marton +2 more
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Neural Networks With Motivation [PDF]
Frontiers in Systems Neuroscience, 2021 Animals rely on internal motivational states to make decisions. The role of motivational salience in decision making is in early stages of mathematical understanding. Here, we propose a reinforcement learning framework that relies on neural networks to learn optimal ongoing behavior for dynamically changing motivation values. First, we show that neural
Sergey A. Shuvaev +5 more
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Medical imaging analysis with artificial neural networks [PDF]
, 2010 Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection ...
Jiang, J., Ren, Jinchang, Trundle, P.
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Neural network approximation [PDF]
Acta Numerica, 2021 Neural networks (NNs) are the method of choice for building learning algorithms. They are now being investigated for other numerical tasks such as solving high-dimensional partial differential equations. Their popularity stems from their empirical success on several challenging learning problems (computer chess/Go, autonomous navigation, face ...
Ronald A. DeVore +2 more
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Analyzing Echo-state Networks Using Fractal Dimension [PDF]
, 2022 This work joins aspects of reservoir optimization, information-theoretic optimal encoding, and at its center fractal analysis. We build on the observation that, due to the recursive nature of recurrent neural networks, input sequences appear as fractal ...
Obst, Oliver +3 more
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Operational neural networks [PDF]
Neural Computing and Applications, 2020 AbstractFeed-forward, fully connected artificial neural networks or the so-called multi-layer perceptrons are well-known universal approximators. However, their learning performance varies significantly depending on the function or the solution space that they attempt to approximate. This is mainly because of their homogenous configuration based solely
Serkan Kiranyaz +3 more
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Lazy training of radial basis neural networks [PDF]
, 2006 Proceeding of: 16th International Conference on Artificial Neural Networks, ICANN 2006. Athens, Greece, September 10-14, 2006Usually, training data are not evenly distributed in the input space.
Galván, Inés M. +5 more
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Evolutionary cellular configurations for designing feed-forward neural networks architectures [PDF]
, 2001 Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001In the recent years, the interest to develop automatic methods to determine appropriate architectures of feed-forward ...
Gutiérrez Sánchez, Germán +6 more
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Approximation in shift-invariant spaces with deep ReLU neural networks [PDF]
, 2022 We study the expressive power of deep ReLU neural networks for approximating functions in dilated shift-invariant spaces, which are widely used in signal processing, image processing, communications and so on.
Wang, Yang, Li, Zhen, Yang, Yunfei
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CoRR, 2018 We present N2Net, a system that implements binary neural networks using commodity switching chips deployed in network switches and routers. Our system shows that these devices can run simple neural network models, whose input is encoded in the network packets' header, at packet processing speeds (billions of packets per second).
Giuseppe Siracusano, Roberto Bifulco
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