Results 11 to 20 of about 27,765 (261)
Quantum Algorithms for Feedforward Neural Networks [PDF]
ACM Transactions on Quantum Computing, 2020 Quantum machine learning has the potential for broad industrial applications, and the development of quantum algorithms for improving the performance of neural networks is of particular interest given the central role they play in machine learning today.
Jonathan Allcock +3 more
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A feedforward unitary equivariant neural network
Neural Networks, 2023 We devise a new type of feedforward neural network. It is equivariant with respect to the unitary group $U(n)$. The input and output can be vectors in $\mathbb{C}^n$ with arbitrary dimension $n$. No convolution layer is required in our implementation. We avoid errors due to truncated higher order terms in Fourier-like transformation. The implementation
Ma, Pui-Wai, Chan, T.-H. Hubert
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The capacity of feedforward neural networks [PDF]
Neural Networks, 2019 A long standing open problem in the theory of neural networks is the development of quantitative methods to estimate and compare the capabilities of different architectures. Here we define the capacity of an architecture by the binary logarithm of the number of functions it can compute, as the synaptic weights are varied. The capacity provides an upper
Pierre Baldi, Roman Vershynin
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Array, 2022 Probabilistic neural network is a variant of feedforward neural network models and has been successfully applied for various pattern classification purposes.
Tetsuya Hoya
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مجلة بغداد للعلوم, 2023 These days, it is crucial to discern between different types of human behavior, and artificial intelligence techniques play a big part in that. The characteristics of the feedforward artificial neural network (FANN) algorithm and the genetic algorithm ...
Ivan V. Stepanyan, Safa A. Hameed
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IEEE Access, 2021 Aiming at the large error of traditional reliability prediction method, and the defects of BP neural network prediction method, a new method of optimized cascade feedforward neural network was proposed based on Adam algorithm to predict the reliability ...
Wang Xiao Yan, Wang Pin, Lang He
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Topology of Learning in Feedforward Neural Networks [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Understanding how neural networks learn remains one of the central challenges in machine learning research. From random at the start of training, the weights of a neural network evolve in such a way as to be able to perform a variety of tasks, such as classifying images.
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Image Restoration Technology Based on Discrete Neural network
MATEC Web of Conferences, 2015 With the development of computer science and technology, the development of artificial intelligence advances rapidly in the field of image restoration.
Zhou Duoying
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Nonlinear programming with feedforward neural networks [PDF]
IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003 We provide a practical and effective method for solving constrained optimization problems by successively training a multilayer feedforward neural network in a coupled neural-network/objective-function representation. Nonlinear programming problems are easily mapped into this representation which has a simpler and more transparent method of solution ...
Jaques Reifman, Earl E. Feldman
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Pemodelan Ritme Kalori Terbakar Setiap Waktu Selama Bersepeda dengan Feedforward Neural Network
Jurnal ElkomikaABSTRAK Pemanfaatan teknologi dalam bidang kesehatan dan olahraga telah berkembang dengan pesat. Aplikasi dan smartwatch dapat menghitung total kalori terbakar selama berolahraga namun tidak memberikan rincian kalori terbakar setiap detiknya.
MUHAMMAD ICHWAN, SALMAN FATHUL ALFARISYI
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