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Investing Redefined: The Role Of Predictive AI In Web3

Investing Redefined: The Role Of Predictive AI In Web3
ibtimes.co.uk - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from ibtimes.co.uk Daily Mail and Mail on Sunday newspapers.

Karim Chaib , Andre Cronje , Graph Convolutional Networks , Graph Attention Networks , Dear 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 Nadu , United States , Chennai Police , Graph Convolutional Networks , Financial Crimes Enforcement Network Fin , Bank Secrecy Act , Watson Ai Lab , Financial Crimes Enforcement Network , Logistic Regression , Random Forest , Multilayer 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 Recognition , Graph Convolutional Networks , Keleton Data , Tatic 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
techstory.in - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from techstory.in Daily Mail and Mail on Sunday newspapers.

Research Material Analysis , Network Architecture , Graph Neural Network Modeling , Graph Neural Networks , Convolutional Neural Network , Graph Neural Networks Gnns , Machine Learning , Natural Language Processing , Based Architecture , Structure Recognition , Visual Elements , Data Preparation , Different Modalities , Visual Features , Multi Modal Data , Tuning Self Attention Mechanisms , Data Extraction , Learning Material Creation , Unstructured Data , Existing Systems , Integrating Docformer , Data Labeling , Neural Networks , Document Understanding , Handling Various Layouts , Graph Convolutional Networks ,

"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 Mag , Convolutional Autoencoder , Attributed Graph , Graph Convolutional Networks , Graph Representation Learning , Ulti Attributed View , Social Networks ,