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Convolutional Neural Networks for Time Series Classification
2017This article concerns identifying objects generating signals from various sensors. Instead of using traditional hand-made time series features we feed the signals as input channels to a convolutional neural network. The network learned low- and high-level features from data.
Mariusz Zebik +3 more
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Time series convolution kernel estimation
AIP Conference Proceedings, 2018This paper focuses on descriptive analysis of time series using artificial neural networks. We used special type of neural networks architecture called convolutional neural networks to for estimation of convolutional kernels. These kernels were estimated from lookbehind window using Adam backpropagation algorithm and as such can be indicative of future
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Starlikeness properties for convolutions involving hypergeometric series
1998Summary: For ...
Ponnusamy, Saminathan, Rønning, Frode
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Modeling Temporal Patterns with Dilated Convolutions for Time-Series Forecasting
ACM Transactions on Knowledge Discovery From Data, 2022Keqin Li, Kenli Li, Cen Chen
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Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023Deyu Meng, Qi Xie
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Arithmetic Functions, Convolution and Finite Fourier Series
Lecture Notes in Networks and Systems, 2022Vichian Laohakosol
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Convergence of Mock Fourier Series on Generalized Bernoulli Convolutions
Acta Applicandae Mathematicae, 2022Min-Wei Tang, Yan-Song Fu, Zhi-Ying Wen
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On Exponential Series Expansions and Convolutions
The Fibonacci Quarterly, 1983M. E. Cohen, D. L. Hudson
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