Journal: Neurocomputing

Abbreviation

Neurocomputing

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Elsevier

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ISSN

0925-2312
1872-8286

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Publications 1 - 10 of 23
  • Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders
    Item type: Journal Article
    Chao, Manuel Arias; Adey, Bryan T.; Fink, Olga (2021)
    Neurocomputing
    Data-driven fault diagnostics of safety-critical systems often faces the challenge of a complete lack of labeled data from faulty system conditions at training time. Since faults of unknown types can arise during deployment, fault diagnostics in this scenario is an open-set learning problem. Without labels and samples from the possible fault types, the open-set diagnostics problem is typically reformulated as fault detection and fault segmentation tasks. Traditional approaches to these tasks, such as one-class classification and unsupervised clustering, do not typically leverage all the available labeled and unlabeled data in the learning algorithm. As a result, their performance is sub-optimal. In this work, we propose an adapted version of the variational autoencoder (VAE), which leverages all available data at training time and has two new design features: 1) implicit supervision on the latent representation of the healthy conditions and 2) implicit bias in the sampling process. The proposed method induces a compact and informative latent representation, thus enabling good detection and segmentation of previously unseen fault types. In an extensive comparison using two turbofan engine datasets, we demonstrate that the proposed method outperforms other learning strategies and deep learning algorithms, yielding significant performance improvements in fault detection and fault segmentation.
  • Priming cross-session motor imagery classification with a universal deep domain adaptation framework
    Item type: Journal Article
    Zhang, Xin; Miao, Zhengqing; Menon, Carlo; et al. (2023)
    Neurocomputing
    Electroencephalogram (EEG) based motor imagery (MI) brain–computer interfaces (BCI) are widely used in applications related to rehabilitation and external device control. However, due to the non-stationary and low signal-to-noise ratio characteristics of EEG, classifying motor imagery tasks of the same participant from different recording sessions is generally challenging. Whether the classification accuracy of cross-session MI can be improved from the perspective of domain adaptation is a question worth verifying. In this paper, we propose a Siamese deep domain adaptation (SDDA) framework for cross-session MI classification based on mathematical models in domain adaptation theory. The SDDA framework primarily consists of three components: a novel preprocessing method based on domain-invariant features, a maximum mean discrepancy (MMD) loss for aligning source and target domain embedding features, and an improved cosine-based center loss designed to suppress the influence of noise and outliers on the neural network. The SDDA framework has been validated with two classic and popular convolutional neural networks (EEGNet and ConvNet) from BCI research field in two MI EEG public datasets (BCI Competition IV IIA, IIB). Compared with the vanilla EEGNet and ConvNet, the SDDA framework improves the MI classification accuracy by 10.49%, 7.60% respectively in IIA dataset, and 4.59%, 3.35% in IIB dataset. The SDDA not only significantly improves the classification performance of the vanilla networks but also surpasses state-of-the-art transfer learning methods, making it a superior and user-friendly approach for MI classification.
  • Training spiking neural networks to associate spatio-temporal input–output spike patterns
    Item type: Journal Article
    Mohemmed, Ammar; Matsuda, Satoshi; Kasabov, Nikola; et al. (2013)
    Neurocomputing
  • Editorial: Human visual saliency and artificial neural attention in deep learning
    Item type: Other Journal Item
    Wang, Wenguan; Cheng, Ming-Ming; Ling, Haibin; et al. (2022)
    Neurocomputing
  • Deep EndoVO: A recurrent convolutional neural network (RCNN) based visual odometry approach for endoscopic capsule robots
    Item type: Journal Article
    Turan, Mehmet; Almalioglu, Yasin; Araujo, Helder; et al. (2018)
    Neurocomputing
    Ingestible wireless capsule endoscopy is an emerging minimally invasive diagnostic technology for inspection of the GI tract and diagnosis of a wide range of diseases and pathologies. Medical device companies and many research groups have recently made substantial progresses in converting passive capsule endoscopes to active capsule robots, enabling more accurate, precise, and intuitive detection of the location and size of the diseased areas. Since a reliable real time pose estimation functionality is crucial for actively controlled endoscopic capsule robots, in this study, we propose a monocular visual odometry (VO) method for endoscopic capsule robot operations. Our method lies on the application of the deep recurrent convolutional neural networks (RCNNs) for the visual odometry task, where convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are used for the feature extraction and inference of dynamics across the frames, respectively. Detailed analyses and evaluations made on a real pig stomach dataset proves that our system achieves high translational and rotational accuracies for different types of endoscopic capsule robot trajectories.
  • Multi-agent actor-critic with time dynamical opponent model
    Item type: Journal Article
    Tian, Yuan; Kladny, Klaus-Rudolf; Wang, Qin; et al. (2023)
    Neurocomputing
    In multi-agent reinforcement learning, multiple agents learn simultaneously while interacting with a common environment and each other. Since the agents adapt their policies during learning, not only the behavior of a single agent becomes non-stationary, but also the environment as perceived by the agent. This renders it particularly challenging to perform policy improvement. In this paper, we propose to exploit the fact that the agents seek to improve their expected cumulative reward and introduce a novel Time Dynamical Opponent Model (TDOM) to encode the knowledge that the opponent policies tend to improve over time. We motivate TDOM theoretically by deriving a lower bound of the log objective of an individual agent and further propose Multi-Agent Actor-Critic with Time Dynamical Opponent Model (TDOM-AC). We evaluate the proposed TDOM-AC on a differential game and the Multi-agent Particle Environment. We show empirically that TDOM achieves superior opponent behavior prediction during test time. The proposed TDOM-AC methodology outperforms state-of-the-art Actor-Critic methods on the performed tasks in cooperative and especially in mixed cooperative-competitive environments. TDOM-AC results in a more stable training and a faster convergence. Our code is available at https://github.com/Yuantian013/TDOM-AC.
  • Temporal correlations of orientations in natural scenes
    Item type: Journal Article
    Kayser, Christoph; Einhäuser, Wolfgang; König, Peter (2003)
    Neurocomputing
  • PB-GCN: Progressive binary graph convolutional networks for skeleton-based action recognition
    Item type: Journal Article
    Zhao, Mengyi; Dai, Shuling; Zhu, Yanjun; et al. (2022)
    Neurocomputing
    Skeleton-based action recognition is an essential yet challenging visual task, whose accuracy has been remarkably improved due to the successful application of graph convolutional networks (GCNs). However, high computation cost and memory usage hinder their deployment on resource-constrained environment. To deal with the issue, in this paper, we introduce two novel progressive binary graph convolutional network for skeleton-based action recognition PB-GCN and PB-GCN*, which can obtain significant speed-up and memory saving. In PB-GCN, the filters are binarized, and in PB-GCN*, both filters and activations are binary. Specifically, we propose a progressive optimization, i.e., employing ternary models as the initialization of binary GCNs (BGCN) to improve the representational capability of binary models. Moreover, the center loss is exploited to improve the training procedure for better performance. Experimental results on two public benchmarks (i.e., Skeleton-Kinetics and NTU RGB + D) demonstrate that the accuracy of the proposed PB-GCN and PB-GCN* are comparable to their full-precision counterparts and outperforms the state-of-the-art methods, such as BWN, XNOR-Net, and Bi-Real Net.
  • Scalable graph neural network-based framework for identifying critical nodes and links in complex networks
    Item type: Journal Article
    Munikoti, Sai; Das, Laya; Natarajan, Balasubramaniam (2022)
    Neurocomputing
    Identifying critical nodes and links in graphs is a crucial task. These nodes/links typically represent critical elements/communication links that play a key role in a system's performance. However, a majority of the methods available in the literature on the identification of critical nodes/links are based on an iterative approach that explores each node/link of a graph at a time, repeating for all nodes/links in the graph. Such methods suffer from high computational complexity and the resulting analysis is also network specific. To overcome these challenges, this article proposes a scalable and generic graph neural network (GNN) based framework for identifying critical nodes/links in large complex networks. The proposed framework defines a GNN based model that learns the node/link criticality score on a small representative subset of nodes/links. An appropriately trained model can be employed to predict the scores of unseen nodes/links in large graphs and consequently identify the most critical ones. The scalability of the framework is demonstrated through prediction of nodes/links scores in large scale synthetic and real-world networks. The proposed approach is fairly accurate in approximating the criticality scores and offers a significant computational advantage over conventional approaches. (c) 2021 Elsevier B.V. All rights reserved.
Publications 1 - 10 of 23