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Investing Redefined: The Role Of Predictive AI In Web3

Investing Redefined: The Role Of Predictive AI In Web3
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Karim chaibAndre cronjeGraph convolutional networksGraph attention networksDear life

Here s how AI is tackling money laundering through cryptocurrency

Here s how AI is tackling money laundering through cryptocurrency
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Tamil naduUnited statesChennai policeGraph convolutional networksFinancial crimes enforcement network finBank secrecy actWatson ai labFinancial crimes enforcement networkLogistic regressionRandom forestMultilayer perceptrons

Static graph convolution with learned temporal and channel-wise graph by Chuankun Li, Shuai Li et al

Graph convolutional networks (GCNs) are widely used in skeleton-based action recognition. It is known that the graph topology is a vital part in GCNs, and different kinds of graph topologies have been proposed for skeleton-based action recognition, mostly based on a predefined topology and a dynamically learned one. The predefined topology is based on the human intuition for skeleton (the connectivity of joints) and has not been investigated whether it is optimal. In this paper, we focus on investigating this static graph topology and propose to generate a learned static graph topology for skeleton. To be specific, a temporal frame-wise and channel-wise topology-based GCNs (TC-GCNs) are developed, where, instead of using a predefined topology by human, a topology is learned for skeleton-based action recognition. The TC-GCNs consist of generating a temporal frame-wise topology and a channel-wise topology to formulate the relationship of skeleton joints in the temporal dimension and chan

Action recognitionGraph convolutional networksKeleton dataTatic graph topology

Data annotation for PDF: The complete educational guide that you need in 2024

Data annotation for PDF: The complete educational guide that you need in 2024
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Research material analysisNetwork architectureGraph neural network modelingGraph neural networksConvolutional neural networkGraph neural networks gnnsMachine learningNatural language processingBased architectureStructure recognitionVisual elementsData preparationDifferent modalitiesVisual featuresMulti modal dataTuning self attention mechanisms

Deep Multi-Attributed-View Graph Representation Learning by Xiaoxiao Ma, Shan Xue et al

Graph representation learning aims at mapping a graph into a lower-dimensional feature space. Deep attributed graph representation, utilizing deep learning models on the graph structure and attributes, shows its significance in mining complex relational data. Most existing deep attributed graph representation models assume graph attributes in a single-attributed view. However, rich information in real-world applications demands the ability to handle multiple attributed views. For example, in social network users' profiles and posts represent two distinct attributed views. A single-attributed view or a simple ensemble of them fails to represent the rich information and complex relations therein. To confront this challenge, this paper proposes a novel deep unsupervised graph representation learning model, called Multi-attributed-view graph Convolutional AutoEncoder (MagCAE). MagCAE learns the node-pairwise proximity among multi-attributed views and node embeddings, across which a no

Autoencoder magConvolutional autoencoderAttributed graphGraph convolutional networksGraph representation learningUlti attributed viewSocial networks