Results 11 to 20 of about 129,225 (287)
Learning cortical hierarchies with temporal Hebbian updates
Frontiers in Computational Neuroscience, 2023 A key driver of mammalian intelligence is the ability to represent incoming sensory information across multiple abstraction levels. For example, in the visual ventral stream, incoming signals are first represented as low-level edge filters and then ...
Pau Vilimelis Aceituno +5 more
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Advanced Intelligent Systems, 2021 On‐chip training of neural networks (NNs) is regarded as a promising training method for neuromorphic systems with analog synaptic devices. Herein, a novel on‐chip training method called direct gradient calculation (DGC) is proposed to substitute ...
Hyungyo Kim +7 more
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Barekeng, 2020 Backpropagation Artificial Neural Network (ANN) is an ANN that uses a supervised learning algorithm. The purpose of this study is to determine the parameters and measure the accuracy of the classification accuracy of the student status of the Open ...
Siti Hadijah Hasanah +1 more
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Face Recognition Using Complex Valued Backpropagation
Jurnal Ilmu Komputer dan Informasi, 2018 Face recognition is one of biometrical research area that is still interesting. This study discusses the Complex-Valued Backpropagation algorithm for face recognition.
Zumrotun Nafisah +2 more
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A regression approach to zebra crossing detection based on convolutional neural networks
IET Cyber-systems and Robotics, 2021 Zebra crossing detection is a fundamental function of the electronic travel aid. It can locate the zebra crossing and estimate its direction to help the visually impaired to cross the road safely.
Xue‐Hua Wu, Renjie Hu, Yu‐Qing Bao
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Prediction of Salinity Based on Meteorological Data Using the Backpropagation Neural Network Method
Ilmu Kelautan, 2021 Salinity is the level of salt dissolved in water. The salinity level of seawater can affect the hydrological balance and climate change. The salinity level of seawater in each area varies depending on the influencing factors, that is evaporation and ...
Anisa Nur Azizah +4 more
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Backpropagation on Dynamical Networks
CoRR, 2022 Dynamical networks are versatile models that can describe a variety of behaviours such as synchronisation and feedback. However, applying these models in real world contexts is difficult as prior information pertaining to the connectivity structure or local dynamics is often unknown and must be inferred from time series observations of network states ...
Eugene Tan +3 more
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Earth and Space Science, 2021 As the amount of published geoscience literature grows, reading and summarizing texts of large collections has become a challenging task. Publication keywords can be considered basic components of knowledge structure representations and have been used to
Qinjun Qiu +3 more
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Water quality prediction method based on preferred classification
IET Cyber-Physical Systems, 2020 Water quality monitoring and prediction are important parts of Cyber Physical Systems. Considering the complexity, diversity, and strong non-linearity of water quality data, a single water quality prediction model is difficult to have a significant ...
Liming Sheng +4 more
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Direct Feedback Alignment with Sparse Connections for Local Learning [PDF]
, 2019 Recent advances in deep neural networks (DNNs) owe their success to training algorithms that use backpropagation and gradient-descent. Backpropagation, while highly effective on von Neumann architectures, becomes inefficient when scaling to large ...
Crafton, Brian +3 more
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