Abstract: Unsupervised pre-training in reinforcement learning enables the agent to gain prior environmental knowledge, which is then fine-tuned in the supervised stage to quickly adapt to various ...

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Traditional approaches to autonomous vehicles (AVs) rely on using millions of miles of driving data in conjunction with even more miles of simulated data as inputs to supervised machine learning ...

This is the code used for unsupervised training of convolutional neural networks as described in the ICML 2017 paper Unsupervised Learning by Predicting Noise (arXiv). The code is composed of two ...

Ilya Sutskever, co-founder of OpenAI, explains why unsupervised learning works and how it relates to supervised learning. The core concept is compression - good compressors can become good predictors.

Abstract: Network representation learning (NRL) has far-reaching effects on data mining research, showing its importance in many real-world applications. NRL, also known as network embedding, aims at ...