Representation search through generate and test

Learning representations from data is one of the funda-mental problems of artificial intelligence and machine learning. Many different approaches exist for learning representations, but what constitutes a good representa-tion 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 ba-sis) 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 ele-ment. Few methods can be used effectively in this case. We show that a search approach to representation learn-ing can naturally fit with this setting. In this approach good representations are searched by generating differ-ent features and then testing them for utility. We de-velop new representation-search methods and show that the generate-and-test approach can be utilized in a sim-ple and effective way for learning representations. Our methods are fully online and add only a small fraction to the overall computation. They constitute an impor-tant step toward effective and inexpensive solutions to representation learning problems