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Found 4 papers, 1 papers with code
Graph Neural Network Approach to Semantic Type Detection in Tables
This study addresses the challenge of detecting semantic column types in relational tables, a key task in many real-world applications.
Representation Extraction and Deep Neural Recommendation for Collaborative Filtering
Although a few works have investigated the application of deep neural networks in recommendation domain, they mostly extract entity features by exploiting unstructured auxiliary data such as visual and textual information, and when it comes to using user-item rating matrix, feature extraction is done by using matrix factorization.
U-CNNpred: A Universal CNN-based Predictor for Stock Markets
In this paper, we investigate the importance of extracting such general features in stock market prediction domain and show how it can improve the performance of financial market prediction.
CNNPred: CNN-based stock market prediction using several data sources
Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested.
