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Yadong Liu

Also published under: Yading Liu

Publications
39
Citations
465
Publications by Year
20072025
Co-Authors:
Show All Co-Authors (44)
Image of author Yadong Liu

Yadong Liu

Also published under: Yading Liu

Affiliation

College of Intelligence Science and Technology
National University of Defense Technology
Changsha, China

Publication Topics

Biography

Yadong Liu was born in Gansu, China, in 1977. He received the B.Sc. and Ph.D. degrees from the National University of Defense Technology, Changsha, China, in 2000 and 2006, respectively. His research interests include image/signal processing, computer/biological vision, system identification and control, and cognitive science.(Based on document published on 2 November 2017).
Publications
39
Citations
465
Publications by Year
20072025
Co-Authors:
Show All Co-Authors (44)
Author's Published Works

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File Classification Based on Spiking Neural Networks

Ana Stanojevic;Giovanni Cherubini;Timoleon Moraitis;Abu Sebastian

2020 IEEE International Symposium on Circuits and Systems (ISCAS)
Year: 2020 | Conference Paper |
Cited by: Papers (2)
In this paper, we propose a system for file classification in large data sets based on spiking neural networks (SNNs). File information contained in key-value metadata pairs is mapped by a novel correlative temporal encoding scheme to spike patterns that are input to an SNN. The correlation between input spike patterns is determined by a file similarity measure. Unsupervised training of such netwo...Show More

File Classification Based on Spiking Neural Networks

Ana Stanojevic;Giovanni Cherubini;Timoleon Moraitis;Abu Sebastian

Year: 2020 | Conference Paper |

Spiking Neural Networks Enable Two-Dimensional Neurons and Unsupervised Multi-Timescale Learning

Timoleon Moraitis;Abu Sebastian;Evangelos Eleftheriou

2018 International Joint Conference on Neural Networks (IJCNN)
Year: 2018 | Conference Paper |
Cited by: Papers (2)
The capabilities of artificial neural networks (ANNs) are limited by the operations possible at their individual neurons and synapses. For instance, each neuron's activation only represents a single scalar variable. In addition, because neuronal activations may be dominated by a single timescale in the synaptic input, unsupervised learning from data with multiple timescales has not been generally ...Show More

Spiking Neural Networks Enable Two-Dimensional Neurons and Unsupervised Multi-Timescale Learning

Timoleon Moraitis;Abu Sebastian;Evangelos Eleftheriou

Year: 2018 | Conference Paper |

The Role of Short-Term Plasticity in Neuromorphic Learning: Learning from the Timing of Rate-Varying Events with Fatiguing Spike-Timing-Dependent Plasticity

Timoleon Moraitis;Abu Sebastian;Evangelos Eleftheriou

IEEE Nanotechnology Magazine
Year: 2018 | Volume: 12, Issue: 3 | Magazine Article |
Cited by: Papers (12)
Neural networks (NNs) have been able to provide record-breaking performance in several machine-learning tasks, such as image and speech recognition, natural-language processing, playing complex games, and data analytics for scientific or business purposes [1]. They process their inputs through a series of linear and nonlinear operations and use learning algorithms, i.e., rules that optimize the pa...Show More

The Role of Short-Term Plasticity in Neuromorphic Learning: Learning from the Timing of Rate-Varying Events with Fatiguing Spike-Timing-Dependent Plasticity

Timoleon Moraitis;Abu Sebastian;Evangelos Eleftheriou

Year: 2018 | Volume: 12, Issue: 3 | Magazine Article |

On-chip unsupervised learning in winner-take-all networks of spiking neurons

Raphaela Kreiser;Timoleon Moraitis;Yulia Sandamirskaya;Giacomo Indiveri

2017 IEEE Biomedical Circuits and Systems Conference (BioCAS)
Year: 2017 | Conference Paper |
Cited by: Papers (17)
The ability to learn re-occurring patterns in real-time sensory inputs in an unsupervised way is a key feature of neural networks that can enable them to carry out complex tasks directly, or to simplify the learning process of subsequent layers in powerful deep network configurations. Dedicated neuromorphic computing electronic systems can implement low-power real-time neural network inference eng...Show More

On-chip unsupervised learning in winner-take-all networks of spiking neurons

Raphaela Kreiser;Timoleon Moraitis;Yulia Sandamirskaya;Giacomo Indiveri

Year: 2017 | Conference Paper |

An efficient synaptic architecture for artificial neural networks

Irem Boybat;Manuel Le Gallo;S.R. Nandakumar;Timoleon Moraitis;Tomas Tuma;Bipin Rajendran;Yusuf Leblebici;Abu Sebastian;Evangelos Eleftheriou

2017 17th Non-Volatile Memory Technology Symposium (NVMTS)
Year: 2017 | Conference Paper |
Cited by: Papers (1)
Artificial neural networks (ANN) have revolutionized the field of machine learning by providing impressive human-like performance in solving real-world tasks in computer vision, speech recognition, or complex strategic games. There is a significant interest in developing non-von Neumann coprocessors for the training of ANNs, where resistive memory devices serve as synaptic elements. However, inter...Show More

An efficient synaptic architecture for artificial neural networks

Irem Boybat;Manuel Le Gallo;S.R. Nandakumar;Timoleon Moraitis;Tomas Tuma;Bipin Rajendran;Yusuf Leblebici;Abu Sebastian;Evangelos Eleftheriou

Year: 2017 | Conference Paper |

Stochastic weight updates in phase-change memory-based synapses and their influence on artificial neural networks

Irem Boybat;Manuel Le Gallo;Timoleon Moraitis;Yusuf Leblebici;Abu Sebastian;Evangelos Eleftheriou

2017 13th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME)
Year: 2017 | Conference Paper |
Cited by: Papers (16)
Artificial neural networks (ANN) have become a powerful tool for machine learning. Resistive memory devices can be used for the realization of a non-von Neumann computational platform for ANN training in an area-efficient way. For instance, the conductance values of phase-change memory (PCM) devices can be used to represent synaptic weights and can be updated in-situ according to learning rules. H...Show More

Stochastic weight updates in phase-change memory-based synapses and their influence on artificial neural networks

Irem Boybat;Manuel Le Gallo;Timoleon Moraitis;Yusuf Leblebici;Abu Sebastian;Evangelos Eleftheriou

Year: 2017 | Conference Paper |

Fatiguing STDP: Learning from spike-timing codes in the presence of rate codes

Timoleon Moraitis;Abu Sebastian;Irem Boybat;Manuel Le Gallo;Tomas Tuma;Evangelos Eleftheriou

2017 International Joint Conference on Neural Networks (IJCNN)
Year: 2017 | Conference Paper |
Cited by: Papers (6)
Spiking neural networks (SNNs) could play a key role in unsupervised machine learning applications, by virtue of strengths related to learning from the fine temporal structure of event-based signals. However, some spike-timing-related strengths of SNNs are hindered by the sensitivity of spike-timing-dependent plasticity (STDP) rules to input spike rates, as fine temporal correlations may be obstru...Show More

Fatiguing STDP: Learning from spike-timing codes in the presence of rate codes

Timoleon Moraitis;Abu Sebastian;Irem Boybat;Manuel Le Gallo;Tomas Tuma;Evangelos Eleftheriou

Year: 2017 | Conference Paper |

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