Random long-range connections induce activity of complex Hindmarsh–Rose neural networks

2008 ◽  
Vol 387 (8-9) ◽  
pp. 2155-2160 ◽  
Author(s):  
Du Qu Wei ◽  
Xiao Shu Luo ◽  
Ying Hua Qin
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Induce Activity

scholarly journals Long-Range Pose Estimation for Aerial Refueling Approaches Using Deep Neural Networks

2020 ◽  
Vol 17 (11) ◽  
pp. 634-646
Author(s):  
Andrew Lee ◽  
Will Dallmann ◽  
Scott Nykl ◽  
Clark Taylor ◽  
Brett Borghetti
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Pose Estimation ◽  
Deep Neural Networks ◽  
Aerial Refueling

scholarly journals DEEPCON: protein contact prediction using dilated convolutional neural networks with dropout

2019 ◽  
Vol 36 (2) ◽  
pp. 470-477 ◽  
Author(s):  
Badri Adhikari
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Convolutional Neural Networks ◽  
High Throughput Sequencing ◽  
State Of The Art ◽  
Deep Convolutional Neural Networks ◽  
Contact Prediction ◽  
Learning Techniques ◽  
Medium Range ◽  
Art Methods

Abstract Motivation Exciting new opportunities have arisen to solve the protein contact prediction problem from the progress in neural networks and the availability of a large number of homologous sequences through high-throughput sequencing. In this work, we study how deep convolutional neural networks (ConvNets) may be best designed and developed to solve this long-standing problem. Results With publicly available datasets, we designed and trained various ConvNet architectures. We tested several recent deep learning techniques including wide residual networks, dropouts and dilated convolutions. We studied the improvements in the precision of medium-range and long-range contacts, and compared the performance of our best architectures with the ones used in existing state-of-the-art methods. The proposed ConvNet architectures predict contacts with significantly more precision than the architectures used in several state-of-the-art methods. When trained using the DeepCov dataset consisting of 3456 proteins and tested on PSICOV dataset of 150 proteins, our architectures achieve up to 15% higher precision when L/2 long-range contacts are evaluated. Similarly, when trained using the DNCON2 dataset consisting of 1426 proteins and tested on 84 protein domains in the CASP12 dataset, our single network achieves 4.8% higher precision than the ensembled DNCON2 method when top L long-range contacts are evaluated. Availability and implementation DEEPCON is available at https://github.com/badriadhikari/DEEPCON/.

scholarly journals Graph Attention Based Proposal 3D ConvNets for Action Detection

2020 ◽  
Vol 34 (04) ◽  
pp. 4626-4633 ◽  
Author(s):  
Jin Li ◽  
Xianglong Liu ◽  
Zhuofan Zong ◽  
Wanru Zhao ◽  
Mingyuan Zhang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Convolutional Neural Networks ◽  
Adjacency Matrix ◽  
State Of The Art ◽  
Original Model ◽  
Action Detection ◽  
Model Experiments ◽  
Temporal And Spatial ◽  
Temporal Action

The recent advances in 3D Convolutional Neural Networks (3D CNNs) have shown promising performance for untrimmed video action detection, employing the popular detection framework that heavily relies on the temporal action proposal generations as the input of the action detector and localization regressor. In practice the proposals usually contain strong intra and inter relations among them, mainly stemming from the temporal and spatial variations in the video actions. However, most of existing 3D CNNs ignore the relations and thus suffer from the redundant proposals degenerating the detection performance and efficiency. To address this problem, we propose graph attention based proposal 3D ConvNets (AGCN-P-3DCNNs) for video action detection. Specifically, our proposed graph attention is composed of intra attention based GCN and inter attention based GCN. We use intra attention to learn the intra long-range dependencies inside each action proposal and update node matrix of Intra Attention based GCN, and use inter attention to learn the inter dependencies between different action proposals as adjacency matrix of Inter Attention based GCN. Afterwards, we fuse intra and inter attention to model intra long-range dependencies and inter dependencies simultaneously. Another contribution is that we propose a simple and effective framewise classifier, which enhances the feature presentation capabilities of backbone model. Experiments on two proposal 3D ConvNets based models (P-C3D and P-ResNet) and two popular action detection benchmarks (THUMOS 2014, ActivityNet v1.3) demonstrate the state-of-the-art performance achieved by our method. Particularly, P-C3D embedded with our module achieves average mAP 3.7% improvement on THUMOS 2014 dataset compared to original model.

Learning intents behind interactions with high-order graph for session-based intelligent recommendation

2021 ◽  
pp. 1-13
Author(s):  
Jianfeng Wang ◽  
Ruomei Wang ◽  
Shaohui Liu
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Higher Order ◽  
High Order ◽  
Sequential Patterns ◽  
Attention Networks ◽  
Graph Neural Networks ◽  
Contextual Interactions ◽  
User Actions ◽  
Inherent Ambiguity

Session-based recommendation is an overwhelming task owing to the inherent ambiguity in anonymous behaviors. Graph convolutional neural networks are receiving wide attention for session-based recommendation research for the sake of their ability to capture the complex transitions of interactions between sessions. Recent research on session-based recommendations mainly focuses on sequential patterns by utilizing graph neural networks. However, it is undeniable that proposed methods are still difficult to capture higher-order interactions between contextual interactions in the same session and has room for improvement. To solve it, we propose a new method based on graph attention mechanism and target oriented items to effectively propagate information, HOGAN for brevity. Higher-order graph attention networks are used to select the importance of different neighborhoods in the graph that consists of a sequence of user actions for recommendation applications. The complementarity between high-order networks is adopted to aggregate and propagate useful signals from the long distant neighbors to solve the long-range dependency capturing problem. Experimental results consistently display that HOGAN has a significantly improvement to 71.53% on precision for the Yoochoose1_64 dataset and enhances the property of the session-based recommendation task.

Graph Deep Learning for Long Range Forecasting

2021 ◽  
Author(s):  
Salva Rühling Cachay ◽  
Emma Erickson ◽  
Arthur Fender C. Bucker ◽  
Ernest Pokropek ◽  
Willa Potosnak ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Long Range ◽  
Large Scale ◽  
State Of The Art ◽  
Southern Oscillation ◽  
Predictive Skill ◽  
Anomaly Pattern ◽  
Previous State ◽  
Graph Neural Networks

<p>Deep learning-based models have been recently shown to be competitive with, or even outperform, state-of-the-art long range forecasting models, such as for projecting the El Niño-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which are difficult to interpret and can fail to model large-scale dependencies, such as teleconnections, that are particularly important for long range projections. Hence, we propose to explicitly model large-scale dependencies with Graph Neural Networks (GNN) to enhance explainability and improve the predictive skill of long lead time forecasts.</p><p>In preliminary experiments focusing on ENSO, our GNN model outperforms previous state-of-the-art machine learning based systems for forecasts up to 6 months ahead. The explicit modeling of information flow via edges makes our model more explainable, and it is indeed shown to learn a sensible graph structure from scratch that correlates with the ENSO anomaly pattern for a given number of lead months.</p><p> </p>

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