Learning representations from data is one of the fundamental problems of artificial intelligence and machine learning. Many different approaches exist for learning representations, but what constitutes a good representation is not yet well understood. In this work, we view the problem of representation learning as one of learning features (e.g., hidden units of neural networks) such that performance of the underlying base system continually improves. We study an important case where learning is done fully online (i.e., on an example-by-example basis) from an unending stream of data. In the presence of an unending stream of data, the computational cost of the learning element should not grow with time and cannot be much more than that of the performance element. Few methods can be used effectively in this case. We show that a search approach to representation learning can naturally fit with this setting. In this approach good representations are searched by generating different features and then testing them for utility. We develop new representation-search methods and show that the generate-and-test approach can be utilized in a simple and effective way for learning representations. Our methods are fully online and add only a small fraction to the overall computation. They constitute an important step toward effective and inexpensive solutions to representation learning problems.

Learning representations from data is one of the fundamental problems of artificial intelligence and machine learning. Many different approaches exist for learning representations, but what constitutes a good representation is not yet well understood. In this work, we view the problem of representation learning as one of learning features (e.g., hidden units of neural networks) such that performance of the underlying base system continually improves. We study an important case where learning is done fully online (i.e., on an example-by-example basis) from an unending stream of data, and the computational cost of the learning element should not grow with time or cannot be much more than that of the performance element. Few methods can be used effectively in this case. We show that a search approach to representation learning can naturally fit with this setting. In this approach good representations are searched by generating different features and then testing them for utility. We develop new representation-search methods and show that the generate-and-test approach can be utilized in a simple and effective way for continually improving representations. Our methods are fully online and add only a small fraction to the overall computation. We believe online representation search constitutes an important step toward effective and inexpensive solutions to representation learning problems.

This paper covers two topics: first an introduction to Algorithmic Complexity Theory: how it defines probability, some of its characteristic properties and past successful applications. Second, we apply it to problems in A.I. - where it promises to give near optimum search procedures for two very broad classes of problems.