SketchGNN: Semantic Sketch Segmentation with Graph Neural Networks

We introduce SketchGNN , a convolutional graph neural network for semantic segmentation and labeling of freehand vector sketches. We treat an input stroke-based sketch as a graph with nodes representing the sampled points along input strokes and edges encoding the stroke structure information. To predict the per-node labels, our SketchGNN uses graph convolution and a static-dynamic branching network architecture to extract the features at three levels, i.e., point-level, stroke-level, and sketch-level. SketchGNN significantly improves the accuracy of the state-of-the-art methods for semantic sketch segmentation (by 11.2% in the pixel-based metric and 18.2% in the component-based metric over a large-scale challenging SPG dataset) and has magnitudes fewer parameters than both image-based and sequence-based methods.

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Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Sensors ◽

10.3390/s21082852 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2852

Author(s):

Parvathaneni Naga Srinivasu ◽

Jalluri Gnana SivaSai ◽

Muhammad Fazal Ijaz ◽

Akash Kumar Bhoi ◽

Wonjoon Kim ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Skin Disease ◽

Network Architecture ◽

Large Scale ◽

Short Term Memory ◽

Convolutional Networks ◽

Occurrence Matrix

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Hybrid Graph Neural Networks for Crowd Counting

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i07.6839 ◽

2020 ◽

Vol 34 (07) ◽

pp. 11693-11700 ◽

Cited By ~ 2

Author(s):

Ao Luo ◽

Fan Yang ◽

Xin Li ◽

Dong Nie ◽

Zhicheng Jiao ◽

...

Keyword(s):

Network Architecture ◽

Message Passing ◽

Large Scale ◽

State Of The Art ◽

Density Variation ◽

Feature Maps ◽

Crowd Counting ◽

Multi Scale ◽

Crowd Density ◽

Graph Neural Networks

Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations within a unified network architecture is still a challenging problem. In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to jointly represent the task-specific feature maps of different scales as nodes, and two types of relations as edges: (i) multi-scale relations capturing the feature dependencies across scales and (ii) mutual beneficial relations building bridges for the cooperation between counting and localization. Thus, through message passing, HyGnn can capture and distill richer relations between nodes to obtain more powerful representations, providing robust and accurate results. Our HyGnn performs significantly well on four challenging datasets: ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming the state-of-the-art algorithms by a large margin.

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Towards Heterogeneous Multi-Agent Reinforcement Learning with Graph Neural Networks

10.5753/eniac.2020.12161 ◽

2020 ◽

Author(s):

Douglas Meneghetti ◽

Reinaldo Bianchi

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Architecture ◽

Communication Channels ◽

Neural Network Architecture ◽

Graph Representations ◽

Labeled Graph ◽

Multiple Agent ◽

Multi Agent ◽

Graph Neural Networks

This work proposes a neural network architecture that learns policies for multiple agent classes in a heterogeneous multi-agent reinforcement setting. The proposed network uses directed labeled graph representations for states, encodes feature vectors of different sizes for different entity classes, uses relational graph convolution layers to model different communication channels between entity types and learns distinct policies for different agent classes, sharing parameters wherever possible. Results have shown that specializing the communication channels between entity classes is a promising step to achieve higher performance in environments composed of heterogeneous entities.

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Fast and Deep Graph Neural Networks

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5803 ◽

2020 ◽

Vol 34 (04) ◽

pp. 3898-3905 ◽

Cited By ~ 4

Author(s):

Claudio Gallicchio ◽

Alessio Micheli

Keyword(s):

Neural Network ◽

Dynamical System ◽

Neural Networks ◽

State Of The Art ◽

Input Graph ◽

Sparse Networks ◽

Set Up ◽

The Stability ◽

Graph Neural Networks ◽

Architectural Organization

We address the efficiency issue for the construction of a deep graph neural network (GNN). The approach exploits the idea of representing each input graph as a fixed point of a dynamical system (implemented through a recurrent neural network), and leverages a deep architectural organization of the recurrent units. Efficiency is gained by many aspects, including the use of small and very sparse networks, where the weights of the recurrent units are left untrained under the stability condition introduced in this work. This can be viewed as a way to study the intrinsic power of the architecture of a deep GNN, and also to provide insights for the set-up of more complex fully-trained models. Through experimental results, we show that even without training of the recurrent connections, the architecture of small deep GNN is surprisingly able to achieve or improve the state-of-the-art performance on a significant set of tasks in the field of graphs classification.

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CapsTM: capsule network for Chinese medical text matching

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01442-9 ◽

2021 ◽

Vol 21 (S2) ◽

Author(s):

Xiaoming Yu ◽

Yedan Shen ◽

Yuan Ni ◽

Xiaowei Huang ◽

Xiaolong Wang ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Deep Learning ◽

Network Architecture ◽

State Of The Art ◽

Interaction Matrix ◽

Application Systems ◽

Input Layer ◽

Art Methods ◽

Text Matching

Abstract Background Text Matching (TM) is a fundamental task of natural language processing widely used in many application systems such as information retrieval, automatic question answering, machine translation, dialogue system, reading comprehension, etc. In recent years, a large number of deep learning neural networks have been applied to TM, and have refreshed benchmarks of TM repeatedly. Among the deep learning neural networks, convolutional neural network (CNN) is one of the most popular networks, which suffers from difficulties in dealing with small samples and keeping relative structures of features. In this paper, we propose a novel deep learning architecture based on capsule network for TM, called CapsTM, where capsule network is a new type of neural network architecture proposed to address some of the short comings of CNN and shows great potential in many tasks. Methods CapsTM is a five-layer neural network, including an input layer, a representation layer, an aggregation layer, a capsule layer and a prediction layer. In CapsTM, two pieces of text are first individually converted into sequences of embeddings and are further transformed by a highway network in the input layer. Then, Bidirectional Long Short-Term Memory (BiLSTM) is used to represent each piece of text and attention-based interaction matrix is used to represent interactive information of the two pieces of text in the representation layer. Subsequently, the two kinds of representations are fused together by BiLSTM in the aggregation layer, and are further represented with capsules (vectors) in the capsule layer. Finally, the prediction layer is a connected network used for classification. CapsTM is an extension of ESIM by adding a capsule layer before the prediction layer. Results We construct a corpus of Chinese medical question matching, which contains 36,360 question pairs. This corpus is randomly split into three parts: a training set of 32,360 question pairs, a development set of 2000 question pairs and a test set of 2000 question pairs. On this corpus, we conduct a series of experiments to evaluate the proposed CapsTM and compare it with other state-of-the-art methods. CapsTM achieves the highest F-score of 0.8666. Conclusion The experimental results demonstrate that CapsTM is effective for Chinese medical question matching and outperforms other state-of-the-art methods for comparison.

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Rumor Detection Based on SAGNN: Simplified Aggregation Graph Neural Networks

Machine Learning and Knowledge Extraction ◽

10.3390/make3010005 ◽

2021 ◽

Vol 3 (1) ◽

pp. 84-94

Author(s):

Liang Zhang ◽

Jingqun Li ◽

Bin Zhou ◽

Yan Jia

Keyword(s):

Neural Network ◽

Neural Networks ◽

Network Architecture ◽

Network Architectures ◽

Neural Network Architecture ◽

Convolutional Networks ◽

Effective Manner ◽

Deep Learning Network ◽

Graph Neural Networks ◽

Rumor Detection

Identifying fake news on media has been an important issue. This is especially true considering the wide spread of rumors on popular social networks such as Twitter. Various kinds of techniques have been proposed for automatic rumor detection. In this work, we study the application of graph neural networks for rumor classification at a lower level, instead of applying existing neural network architectures to detect rumors. The responses to true rumors and false rumors display distinct characteristics. This suggests that it is essential to capture such interactions in an effective manner for a deep learning network to achieve better rumor detection performance. To this end we present a simplified aggregation graph neural network architecture. Experiments on publicly available Twitter datasets demonstrate that the proposed network has performance on a par with or even better than that of state-of-the-art graph convolutional networks, while significantly reducing the computational complexity.

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ThriftyNets: Convolutional Neural Networks with Tiny Parameter Budget

IoT ◽

10.3390/iot2020012 ◽

2021 ◽

Vol 2 (2) ◽

pp. 222-235

Author(s):

Guillaume Coiffier ◽

Ghouthi Boukli Hacene ◽

Vincent Gripon

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Convolutional Neural Network ◽

Spatial Resolution ◽

Network Architecture ◽

Deep Neural Networks ◽

State Of The Art ◽

Feature Maps ◽

Neural Network Architecture

Deep Neural Networks are state-of-the-art in a large number of challenges in machine learning. However, to reach the best performance they require a huge pool of parameters. Indeed, typical deep convolutional architectures present an increasing number of feature maps as we go deeper in the network, whereas spatial resolution of inputs is decreased through downsampling operations. This means that most of the parameters lay in the final layers, while a large portion of the computations are performed by a small fraction of the total parameters in the first layers. In an effort to use every parameter of a network at its maximum, we propose a new convolutional neural network architecture, called ThriftyNet. In ThriftyNet, only one convolutional layer is defined and used recursively, leading to a maximal parameter factorization. In complement, normalization, non-linearities, downsamplings and shortcut ensure sufficient expressivity of the model. ThriftyNet achieves competitive performance on a tiny parameters budget, exceeding 91% accuracy on CIFAR-10 with less than 40 k parameters in total, 74.3% on CIFAR-100 with less than 600 k parameters, and 67.1% On ImageNet ILSVRC 2012 with no more than 4.15 M parameters. However, the proposed method typically requires more computations than existing counterparts.

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Exploiting Heterogeneous Graph Neural Networks with Latent Worker/Task Correlation Information for Label Aggregation in Crowdsourcing

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3460865 ◽

2022 ◽

Vol 16 (2) ◽

pp. 1-18

Author(s):

Hanlu Wu ◽

Tengfei Ma ◽

Lingfei Wu ◽

Fangli Xu ◽

Shouling Ji

Keyword(s):

Neural Network ◽

Neural Networks ◽

State Of The Art ◽

Superior Performance ◽

True Label ◽

Label Aggregation ◽

Correlation Information ◽

Real World Datasets ◽

Graph Neural Networks ◽

High Level

Crowdsourcing has attracted much attention for its convenience to collect labels from non-expert workers instead of experts. However, due to the high level of noise from the non-experts, a label aggregation model that infers the true label from noisy crowdsourced labels is required. In this article, we propose a novel framework based on graph neural networks for aggregating crowd labels. We construct a heterogeneous graph between workers and tasks and derive a new graph neural network to learn the representations of nodes and the true labels. Besides, we exploit the unknown latent interaction between the same type of nodes (workers or tasks) by adding a homogeneous attention layer in the graph neural networks. Experimental results on 13 real-world datasets show superior performance over state-of-the-art models.

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GraphReach: Position-Aware Graph Neural Network using Reachability Estimations

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/211 ◽

2021 ◽

Author(s):

Sunil Nishad ◽

Shubhangi Agarwal ◽

Arnab Bhattacharya ◽

Sayan Ranu

Keyword(s):

Neural Network ◽

Neural Networks ◽

State Of The Art ◽

Empirical Evaluation ◽

Position Information ◽

Accurate Performance ◽

Anchor Nodes ◽

Approximation Heuristic ◽

Graph Neural Networks ◽

Node Embeddings

Majority of the existing graph neural networks(GNN) learn node embeddings that encode their local neighborhoods but not their positions. Consequently, two nodes that are vastly distant but located in similar local neighborhoods map to similar embeddings in those networks. This limitation prevents accurate performance in predictive tasks that rely on position information. In this paper, we develop GRAPHREACH , a position-aware inductive GNN that captures the global positions of nodes through reachability estimations with respect to a set of anchor nodes. The anchors are strategically selected so that reachability estimations across all the nodes are maximized. We show that this combinatorial anchor selection problem is NP-hard and, consequently, develop a greedy (1−1/e) approximation heuristic. Empirical evaluation against state-of-the-art GNN architectures reveal that GRAPHREACH provides up to 40% relative improvement in accuracy. In addition, it is more robust to adversarial attacks.

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Graph Deep Learning for Long Range Forecasting

10.5194/egusphere-egu21-9141 ◽

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

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 Ni&#241;o-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.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.&#160;

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