The Experts below are selected from a list of 246 Experts worldwide ranked by ideXlab platform

T M Mcginnity - One of the best experts on this subject based on the ideXlab platform.

  • spiketemp an enhanced rank order based learning approach for spiking neural networks with Adaptive Structure
    IEEE Transactions on Neural Networks, 2017
    Co-Authors: Jinling Wang, Ammar Belatreche, Liam Maguire, T M Mcginnity
    Abstract:

    This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically Adaptive Structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

  • an online supervised learning method for spiking neural networks with Adaptive Structure
    Neurocomputing, 2014
    Co-Authors: Jinling Wang, Ammar Belatreche, Liam Maguire, T M Mcginnity
    Abstract:

    A novel online learning algorithm for Spiking Neural Networks (SNNs) with dynamically Adaptive Structure is presented. The main contribution of this work lies in the fact that the proposed Adaptive SNN is able to classify spike-based spatio-temporal inputs after just one presentation of the training set, i.e. in one pass only, and does not require the entire training set to be available at once. Both the Structure and weights of the SNN are learned dynamically through a combination of unsupervised and supervised learning paradigms. The proposed feed-forward SNN consists of three layers of spiking neurons: an input layer which temporally encodes real valued features into spike-based spatio-temporal patterns, a hidden layer of dynamically grown and pruned neurons which perform spatio-temporal clustering, and an output layer for classification. An unsupervised spiking-based clustering algorithm is implemented by the hidden layer whose spiking neurons are trained to compute a temporal Radial Basis Function (RBF) where incoming inputs will selectively activate hidden neurons based on how close the inputs are to the preferred inputs of the hidden neurons. The centre of each hidden RBF spiking neuron is represented by its time to first spike. In addition, a growing and pruning strategy is proposed to adjust the Structure of the hidden layer 'on-the-fly' as inputs are presented to the SNN. Both the weights and the centres of the hidden RBF neurons are learned in an unsupervised way and classification at the output layer is achieved through supervised learning where the learning windows proposed for STDP and anti-STDP are used to adjust the weights of the output neurons afferent connections. Competition at both the hidden and the output layers is achieved through the use of lateral inhibitory connections between the neurons of each layer. The proposed online learning algorithm is validated on several benchmark datasets. The evaluation results demonstrate that SNNs trained with the proposed approach require only one pass through the training set in order to classify the inputs with comparable accuracies to existing SNN-based approaches as well as traditional representative classifiers.

Jinling Wang - One of the best experts on this subject based on the ideXlab platform.

  • spiketemp an enhanced rank order based learning approach for spiking neural networks with Adaptive Structure
    IEEE Transactions on Neural Networks, 2017
    Co-Authors: Jinling Wang, Ammar Belatreche, Liam Maguire, T M Mcginnity
    Abstract:

    This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically Adaptive Structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

  • an online supervised learning method for spiking neural networks with Adaptive Structure
    Neurocomputing, 2014
    Co-Authors: Jinling Wang, Ammar Belatreche, Liam Maguire, T M Mcginnity
    Abstract:

    A novel online learning algorithm for Spiking Neural Networks (SNNs) with dynamically Adaptive Structure is presented. The main contribution of this work lies in the fact that the proposed Adaptive SNN is able to classify spike-based spatio-temporal inputs after just one presentation of the training set, i.e. in one pass only, and does not require the entire training set to be available at once. Both the Structure and weights of the SNN are learned dynamically through a combination of unsupervised and supervised learning paradigms. The proposed feed-forward SNN consists of three layers of spiking neurons: an input layer which temporally encodes real valued features into spike-based spatio-temporal patterns, a hidden layer of dynamically grown and pruned neurons which perform spatio-temporal clustering, and an output layer for classification. An unsupervised spiking-based clustering algorithm is implemented by the hidden layer whose spiking neurons are trained to compute a temporal Radial Basis Function (RBF) where incoming inputs will selectively activate hidden neurons based on how close the inputs are to the preferred inputs of the hidden neurons. The centre of each hidden RBF spiking neuron is represented by its time to first spike. In addition, a growing and pruning strategy is proposed to adjust the Structure of the hidden layer 'on-the-fly' as inputs are presented to the SNN. Both the weights and the centres of the hidden RBF neurons are learned in an unsupervised way and classification at the output layer is achieved through supervised learning where the learning windows proposed for STDP and anti-STDP are used to adjust the weights of the output neurons afferent connections. Competition at both the hidden and the output layers is achieved through the use of lateral inhibitory connections between the neurons of each layer. The proposed online learning algorithm is validated on several benchmark datasets. The evaluation results demonstrate that SNNs trained with the proposed approach require only one pass through the training set in order to classify the inputs with comparable accuracies to existing SNN-based approaches as well as traditional representative classifiers.

Liam Maguire - One of the best experts on this subject based on the ideXlab platform.

  • spiketemp an enhanced rank order based learning approach for spiking neural networks with Adaptive Structure
    IEEE Transactions on Neural Networks, 2017
    Co-Authors: Jinling Wang, Ammar Belatreche, Liam Maguire, T M Mcginnity
    Abstract:

    This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically Adaptive Structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

  • an online supervised learning method for spiking neural networks with Adaptive Structure
    Neurocomputing, 2014
    Co-Authors: Jinling Wang, Ammar Belatreche, Liam Maguire, T M Mcginnity
    Abstract:

    A novel online learning algorithm for Spiking Neural Networks (SNNs) with dynamically Adaptive Structure is presented. The main contribution of this work lies in the fact that the proposed Adaptive SNN is able to classify spike-based spatio-temporal inputs after just one presentation of the training set, i.e. in one pass only, and does not require the entire training set to be available at once. Both the Structure and weights of the SNN are learned dynamically through a combination of unsupervised and supervised learning paradigms. The proposed feed-forward SNN consists of three layers of spiking neurons: an input layer which temporally encodes real valued features into spike-based spatio-temporal patterns, a hidden layer of dynamically grown and pruned neurons which perform spatio-temporal clustering, and an output layer for classification. An unsupervised spiking-based clustering algorithm is implemented by the hidden layer whose spiking neurons are trained to compute a temporal Radial Basis Function (RBF) where incoming inputs will selectively activate hidden neurons based on how close the inputs are to the preferred inputs of the hidden neurons. The centre of each hidden RBF spiking neuron is represented by its time to first spike. In addition, a growing and pruning strategy is proposed to adjust the Structure of the hidden layer 'on-the-fly' as inputs are presented to the SNN. Both the weights and the centres of the hidden RBF neurons are learned in an unsupervised way and classification at the output layer is achieved through supervised learning where the learning windows proposed for STDP and anti-STDP are used to adjust the weights of the output neurons afferent connections. Competition at both the hidden and the output layers is achieved through the use of lateral inhibitory connections between the neurons of each layer. The proposed online learning algorithm is validated on several benchmark datasets. The evaluation results demonstrate that SNNs trained with the proposed approach require only one pass through the training set in order to classify the inputs with comparable accuracies to existing SNN-based approaches as well as traditional representative classifiers.

Ammar Belatreche - One of the best experts on this subject based on the ideXlab platform.

  • spiketemp an enhanced rank order based learning approach for spiking neural networks with Adaptive Structure
    IEEE Transactions on Neural Networks, 2017
    Co-Authors: Jinling Wang, Ammar Belatreche, Liam Maguire, T M Mcginnity
    Abstract:

    This paper presents an enhanced rank-order-based learning algorithm, called SpikeTemp, for spiking neural networks (SNNs) with a dynamically Adaptive Structure. The trained feed-forward SNN consists of two layers of spiking neurons: 1) an encoding layer which temporally encodes real-valued features into spatio-temporal spike patterns and 2) an output layer of dynamically grown neurons which perform spatio-temporal classification. Both Gaussian receptive fields and square cosine population encoding schemes are employed to encode real-valued features into spatio-temporal spike patterns. Unlike the rank-order-based learning approach, SpikeTemp uses the precise times of the incoming spikes for adjusting the synaptic weights such that early spikes result in a large weight change and late spikes lead to a smaller weight change. This removes the need to rank all the incoming spikes and, thus, reduces the computational cost of SpikeTemp. The proposed SpikeTemp algorithm is demonstrated on several benchmark data sets and on an image recognition task. The results show that SpikeTemp can achieve better classification performance and is much faster than the existing rank-order-based learning approach. In addition, the number of output neurons is much smaller when the square cosine encoding scheme is employed. Furthermore, SpikeTemp is benchmarked against a selection of existing machine learning algorithms, and the results demonstrate the ability of SpikeTemp to classify different data sets after just one presentation of the training samples with comparable classification performance.

  • an online supervised learning method for spiking neural networks with Adaptive Structure
    Neurocomputing, 2014
    Co-Authors: Jinling Wang, Ammar Belatreche, Liam Maguire, T M Mcginnity
    Abstract:

    A novel online learning algorithm for Spiking Neural Networks (SNNs) with dynamically Adaptive Structure is presented. The main contribution of this work lies in the fact that the proposed Adaptive SNN is able to classify spike-based spatio-temporal inputs after just one presentation of the training set, i.e. in one pass only, and does not require the entire training set to be available at once. Both the Structure and weights of the SNN are learned dynamically through a combination of unsupervised and supervised learning paradigms. The proposed feed-forward SNN consists of three layers of spiking neurons: an input layer which temporally encodes real valued features into spike-based spatio-temporal patterns, a hidden layer of dynamically grown and pruned neurons which perform spatio-temporal clustering, and an output layer for classification. An unsupervised spiking-based clustering algorithm is implemented by the hidden layer whose spiking neurons are trained to compute a temporal Radial Basis Function (RBF) where incoming inputs will selectively activate hidden neurons based on how close the inputs are to the preferred inputs of the hidden neurons. The centre of each hidden RBF spiking neuron is represented by its time to first spike. In addition, a growing and pruning strategy is proposed to adjust the Structure of the hidden layer 'on-the-fly' as inputs are presented to the SNN. Both the weights and the centres of the hidden RBF neurons are learned in an unsupervised way and classification at the output layer is achieved through supervised learning where the learning windows proposed for STDP and anti-STDP are used to adjust the weights of the output neurons afferent connections. Competition at both the hidden and the output layers is achieved through the use of lateral inhibitory connections between the neurons of each layer. The proposed online learning algorithm is validated on several benchmark datasets. The evaluation results demonstrate that SNNs trained with the proposed approach require only one pass through the training set in order to classify the inputs with comparable accuracies to existing SNN-based approaches as well as traditional representative classifiers.

Xue Zhou - One of the best experts on this subject based on the ideXlab platform.

  • Adaptive graph representation learning for video person re identification
    IEEE Transactions on Image Processing, 2020
    Co-Authors: Omar El Farouk Bourahla, Qi Tian, Xue Zhou
    Abstract:

    Recent years have witnessed the remarkable progress of applying deep learning models in video person re-identification (Re-ID). A key factor for video person Re-ID is to effectively construct discriminative and robust video feature representations for many complicated situations. Part-based approaches employ spatial and temporal attention to extract representative local features. While correlations between parts are ignored in the previous methods, to leverage the relations of different parts, we propose an innovative Adaptive graph representation learning scheme for video person Re-ID, which enables the contextual interactions between relevant regional features. Specifically, we exploit the pose alignment connection and the feature affinity connection to construct an Adaptive Structure-aware adjacency graph, which models the intrinsic relations between graph nodes. We perform feature propagation on the adjacency graph to refine regional features iteratively, and the neighbor nodes’ information is taken into account for part feature representation. To learn compact and discriminative representations, we further propose a novel temporal resolution-aware regularization, which enforces the consistency among different temporal resolutions for the same identities. We conduct extensive evaluations on four benchmarks, i.e. iLIDS-VID, PRID2011, MARS, and DukeMTMC-VideoReID, experimental results achieve the competitive performance which demonstrates the effectiveness of our proposed method. Code is available at https://github.com/weleen/AGRL.pytorch .

  • Adaptive graph representation learning for video person re identification
    arXiv: Computer Vision and Pattern Recognition, 2019
    Co-Authors: Omar El Farouk Bourahla, Qi Tian, Xue Zhou
    Abstract:

    Recent years have witnessed the remarkable progress of applying deep learning models in video person re-identification (Re-ID). A key factor for video person Re-ID is to effectively construct discriminative and robust video feature representations for many complicated situations. Part-based approaches employ spatial and temporal attention to extract representative local features. While correlations between parts are ignored in the previous methods, to leverage the relations of different parts, we propose an innovative Adaptive graph representation learning scheme for video person Re-ID, which enables the contextual interactions between relevant regional features. Specifically, we exploit the pose alignment connection and the feature affinity connection to construct an Adaptive Structure-aware adjacency graph, which models the intrinsic relations between graph nodes. We perform feature propagation on the adjacency graph to refine regional features iteratively, and the neighbor nodes' information is taken into account for part feature representation. To learn compact and discriminative representations, we further propose a novel temporal resolution-aware regularization, which enforces the consistency among different temporal resolutions for the same identities. We conduct extensive evaluations on four benchmarks, i.e. iLIDS-VID, PRID2011, MARS, and DukeMTMC-VideoReID, experimental results achieve the competitive performance which demonstrates the effectiveness of our proposed method. The code is available at this https URL.