Results 21 to 30 of about 354,413 (309)
From Imitation to Prediction, Data Compression vs Recurrent Neural Networks for Natural Language Processing [PDF]
, 2017 In recent studies [1][13][12] Recurrent Neural Networks were used for generative processes and their surprising performance can be explained by their ability to create good predictions. In addition, data compression is also based on predictions. What the
Argerich, Luis +2 more
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Recurrent neural network wave functions
Physical Review Research, 2020 A core technology that has emerged from the artificial intelligence revolution is the recurrent neural network (RNN). Its unique sequence-based architecture provides a tractable likelihood estimate with stable training paradigms, a combination that has ...
Mohamed Hibat-Allah +4 more
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Complexity without chaos: Plasticity within random recurrent networks generates robust timing and motor control [PDF]
, 2012 It is widely accepted that the complex dynamics characteristic of recurrent neural circuits contributes in a fundamental manner to brain function. Progress has been slow in understanding and exploiting the computational power of recurrent dynamics for ...
A Banerjee +53 more
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Clustering Based on Continuous Hopfield Network
Mathematics, 2022 Clustering aims to group n data samples into k clusters. In this paper, we reformulate the clustering problem into an integer optimization problem and propose a recurrent neural network with n×k neurons to solve it. We prove the stability and convergence
Yao Xiao +3 more
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General Recurrent Neural Network for Solving Generalized Linear Matrix Equation
Complexity, 2017 This brief proposes a general framework of the nonlinear recurrent neural network for solving online the generalized linear matrix equation (GLME) with global convergence property. If the linear activation function is utilized, the neural state matrix of
Zhan Li, Hong Cheng, Hongliang Guo
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Sensors, 2021 In this study, a scheme of remaining useful lifetime (RUL) prognosis from raw acoustic emission (AE) data is presented to predict the concrete structure’s failure before its occurrence, thus possibly prolong its service life and minimizing the risk of ...
Tuan-Khai Nguyen +2 more
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CloudScan - A configuration-free invoice analysis system using recurrent neural networks [PDF]
, 2017 We present CloudScan; an invoice analysis system that requires zero configuration or upfront annotation. In contrast to previous work, CloudScan does not rely on templates of invoice layout, instead it learns a single global model of invoices that ...
Laws, Florian +2 more
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Neural Approximators for Variable-Order Fractional Calculus Operators (VO-FC)
IEEE Access, 2022 The paper presents research on the approximation of variable-order fractional operators by recurrent neural networks. The research focuses on two basic variable-order fractional operators, i.e., integrator and differentiator.
Bartosz Puchalski
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Detection of Shoplifting on Video Using a Hybrid Network
Computation, 2022 Shoplifting is a major problem for shop owners and many other parties, including the police. Video surveillance generates huge amounts of information that staff cannot process in real time.
Lyudmyla Kirichenko +3 more
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A new approach to seasonal energy consumption forecasting using temporal convolutional networks
Results in Engineering, 2023 There has been a significant increase in the attention paid to resource management in smart grids, and several energy forecasting models have been published in the literature.
Abdul Khalique Shaikh +4 more
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