Results 11 to 20 of about 751,622 (266)
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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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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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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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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On the Robustness of a Neural Network [PDF]
2017 IEEE 36th Symposium on Reliable Distributed Systems (SRDS), 2017 36th IEEE International Symposium on Reliable Distributed Systems 26 - 29 September 2017.
El Mahdi El Mhamdi +2 more
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The World as a Neural Network [PDF]
Entropy, 2020 We discuss a possibility that the entire universe on its most fundamental level is a neural network. We identify two different types of dynamical degrees of freedom: “trainable” variables (e.g., bias vector or weight matrix) and “hidden” variables (e.g., state vector of neurons).
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Bootstrapping Neural Networks [PDF]
Neural Computation, 2000 Knowledge about the distribution of a statistical estimator is important for various purposes, such as the construction of confidence intervals for model parameters or the determination of critical values of tests. A widely used method to estimate this distribution is the so-called bootstrap, which is based on an imitation of the probabilistic ...
Franke, Jürgen, Neumann, Michael
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Neural network applications [PDF]
Proceedings Electronic Technology Directions to the Year 2000, 2002 Artificial neural networks, also called neural networks, have been used successfully in many fields including engineering, science and business. This paper presents the implementation of several neural network simulators and their applications in character recognition and other engineering ...
Vonk, E., Jain, L.C., Veelenturf, L.P.J.
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Correlational Neural Networks [PDF]
Neural Computation, 2016 Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)–based approaches and autoencoder (AE)–based approaches.
Sarath Chandar +3 more
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