3 articles
OpenAI publishes a semi-supervised knowledge transfer method for deep learning from private training data. The technique combines public and private data while preserving privacy of sensitive data.
OpenAI hosted its first self-organizing conference on machine learning, bringing together over 150 AI practitioners in its offices.
OpenAI develops a deep inverse dynamics model learning approach to transfer simulation-trained policies to real-world robots. The method reduces real-world data requirements by learning to predict actions from observations, improving generalization of simulation-trained policies.