Representation Learning

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• [Reinforcement Learning (RL)]]

Feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task. Feature learning is motivated by the fact that machine learning tasks such as classification often require input that is mathematically and computationally convenient to process. However, real-world data such as images, video, and sensor data has not yielded to attempts to algorithmically define specific features. An alternative is to discover such features or representations through examination, without relying on explicit algorithms. Wikipedia

Representation Learning and Deep Learning

Yoshua Bengio | Institute for Pure & Applied Mathematics (IPAM)

Large-Scale Graph

• Graph Convolutional Network (GCN), Graph Neural Networks (Graph Nets), Geometric Deep Learning