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You are here: home » teaching » 2020winter » lbda » papers

Papers

Data Streams

  • Mondrian Forests: Efficient Online Random Forests
  • Nonconvex online support vector machines
  • Adaptive random forests for evolving data stream classification
  • An ensemble of cluster-based classifiers for semi-supervised classification of non-stationary data streams
  • Extreme learning machine for multilayer perceptron (only available in the Lernraum-Plus)

Web Advertizing

  • Wide & Deep Learning for Recommender Systems - Raheel
  • DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
  • Collaborative deep learning for recommender systems
  • Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising
  • Time-Aware Prospective Modeling of Users for Online Display Advertising

Predicting Links in Social Networks

  • Gravity-inspired graph autoencoders for directed link prediction.
  • Link prediction based on graph neural networks.
  • GCN-GAN: A non-linear temporal link prediction model for weighted dynamic networks.


teaching/2020winter/lbda/papers.txt · Last modified: 2020/11/11 17:18 by dgoebel
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