2017 International Joint Conference on Neural Networks (IJCNN)

14-19 May 2017

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  • Welcome messages

    Publication Year: 2017, Page(s):7 - 9
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  • Organizing committee

    Publication Year: 2017, Page(s):10 - 13
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  • Reviewers

    Publication Year: 2017, Page(s):14 - 18
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  • INNS organization

    Publication Year: 2017, Page(s): 19
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  • IEEE CIS organization

    Publication Year: 2017, Page(s): 20
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  • Plenary talks: Frontiers in recurrent neural network research

    Publication Year: 2017, Page(s):21 - 33
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  • Overview

    Publication Year: 2017, Page(s):1 - 6
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  • Advertisements

    Publication Year: 2017, Page(s):103 - 106
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  • Technical papers

    Publication Year: 2017, Page(s):1 - 55
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  • Author index

    Publication Year: 2017, Page(s):1 - 40
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  • 2017 Conference program

    Publication Year: 2017, Page(s):1 - 106
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  • Venue floor plan

    Publication Year: 2017, Page(s): 101
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  • [Copyright notice]

    Publication Year: 2017, Page(s): 1
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  • Simple and efficient parallelization for probabilistic temporal tensor factorization

    Publication Year: 2017, Page(s):1 - 8
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (232 KB)

    Probabilistic Temporal Tensor Factorization (PTTF) is an effective algorithm to model the temporal tensor data. It leverages a time constraint to capture the evolving properties of tensor data. Nowadays the exploding dataset demands a large scale PTTF analysis, and a parallel solution is critical to accommodate the trend. Whereas, the parallelization of PTTF still remains unexplored. In this paper... View full abstract»

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  • Exploiting sparsity to improve the accuracy of Nyström-based large-scale spectral clustering

    Publication Year: 2017, Page(s):9 - 16
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (258 KB) | HTML iconHTML

    The Nyström method is a matrix approximation technique that has shown great promise in speeding up spectral clustering. However, when the input matrix is sparse, we show that the traditional Nyström method requires a prohibitively large number of samples to obtain a good approximation. We propose a novel sampling approach to select the landmark points used to compute the Nystro�... View full abstract»

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  • Brazil's Bolsa Familia and young adult workers: A parallel RDD approach to large datasets

    Publication Year: 2017, Page(s):17 - 24
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (271 KB) | HTML iconHTML

    Regression-Discontinuity Design is a non-experimental method to estimate the impacts of social welfare programs in situations where the treatment assignment is determined by whether an observed variable (running variable) is above or below a known cutoff point. The main idea behind RDD is that individuals whose running variable is just above or just below the cutoff are similar, and so, any differ... View full abstract»

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  • Advanced pseudo-inverse linear discriminants for the improvement of classification accuracies

    Publication Year: 2017, Page(s):25 - 30
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (278 KB) | HTML iconHTML

    There is very little practicable significance to prove the equivalency between a pseudo-inverse linear discriminant (PILD) with the desired outputs in reverse proportion to the number of within-class samples and a Fisher linear discriminant (FLD) with the totally projected mean thresholds which are disadvantageous to improve the overall classification accuracy. Even if so, several examples have bo... View full abstract»

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  • A self-organizing model for affective memory

    Publication Year: 2017, Page(s):31 - 38
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (425 KB) | HTML iconHTML

    Emotions are related to many different parts of our lives: from the perception of the environment around us to different learning processes and natural communication. Therefore, it is very hard to achieve an automatic emotion recognition system which is adaptable enough to be used in real-world scenarios. This paper proposes the use of a growing and self-organizing affective memory architecture to... View full abstract»

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  • Hyperarticulation aids learning of new vowels in a developmental speech acquisition model

    Publication Year: 2017, Page(s):39 - 45
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (456 KB) | HTML iconHTML

    Many studies emphasize the importance of infant-directed speech: stronger articulated, higher-quality speech helps infants to better distinguish different speech sounds. This effect has been widely investigated in terms of the infant's perceptual capabilities, but few studies examined whether infant-directed speech has an effect on articulatory learning. In earlier studies, we developed a model th... View full abstract»

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  • Neurorobotic simulations on the degradation of multiple column liquid state machines

    Publication Year: 2017, Page(s):46 - 51
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1210 KB) | HTML iconHTML

    Two different configurations of Liquid State Machine (LSM), a special type of Reservoir Computing with internal nodes modelled as spiking neurons, implementing multiple columns (Modular and Monolithic approaches) are tested against the decimation of neurons, connections and entire columns in order to verify which one can better withstand the damage. Based on the neurorobotics outlook, this work is... View full abstract»

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  • The art of scaling up : A computational account on action selection in basal ganglia

    Publication Year: 2017, Page(s):52 - 58
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (394 KB) | HTML iconHTML

    What makes a computational neuronal model `large scale'? Is it the number of neurons modeled? Or the number of brain regions modeled in a network? Most of the higher cognitive processes span across co-ordinated activity in a network of different brain areas. However at the same time, the basic information transfer takes place at a single neuron level, together with multiple other neurons. We explo... View full abstract»

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  • EEG classification based on sparse representation

    Publication Year: 2017, Page(s):59 - 62
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (275 KB) | HTML iconHTML

    Classifying motor imagery brain signals where the signals are obtained based on imagined movement of the limbs is a major step in developing Brain Computer Interfaces (BCIs). Features from a small spatial region are approximated by a sparse linear combination of few atoms from a multi-class dictionary constructed from the features of the electroencephalography (EEG) training signals for each class... View full abstract»

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  • Stochastic and deterministic stationarity analysis of EEG data

    Publication Year: 2017, Page(s):63 - 70
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (706 KB) | HTML iconHTML

    For a long time, EEG has been used for diagnosing mental disorders, for the EEG is an easy technique to acquire such signals. Time series methods are used to study the data since EEG records can be seem as time series. Such methods can be divided into two categories, stochastic and deterministic. Several methods in both categories require the signal to be stationary. Although many works acknowledg... View full abstract»

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  • Enhanced detection of movement onset in EEG through deep oversampling

    Publication Year: 2017, Page(s):71 - 78
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (658 KB) | HTML iconHTML

    A deep learning approach for oversampling of electroencephalography (EEG) recorded during self-paced hand movement is investigated for the purpose of improving EEG classification in general and the detection of movement onset during online Brain-Computer Interfaces in particular. Learning from self-paced EEG data is challenging mainly due to the highly imbalance nature of the data reducing the gen... View full abstract»

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  • Investigating the possibility of applying EEG lossy compression to EEG-based user authentication

    Publication Year: 2017, Page(s):79 - 85
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (658 KB) | HTML iconHTML

    Using EEG signal as a new type of biometric in user authentication systems has been emerging as an interesting research topic. However, one of the major challenges is that a huge amount of EEG data that needs to be processed, transmitted and stored. The use of EEG compression is therefore becoming necessary. In this paper, we investigate the feasibility of using lossy compression to EEG data in EE... View full abstract»

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