EAGER: Toward Scalable Life-long Representation Learning

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Machine learning is a powerful tool for artificial intelligence and data mining problems. However, its success critically relies on a good feature representation of the data; therefore, the problem of feature construction poses a fundamental challenge. In recent years, representation learning has emerged as a promising method for learning useful feature representations from data. However, the current state-of-the-art methods are still limited in building intelligent agents that can learn and interact with complex environments and large amounts of sensory input. Specifically, the majority of the existing methods cannot scale well to large-scale data.The goal of this project is to fill this gap by formulating a new framework that can effectively learn representations from complex environments and scale to large data. Specifically, we propose novel approaches for learning robust representations from large-scale data by (1) controlling the complexity of the feature representations and (2) adaptively modeling relevant patterns in the presence of significant amounts of irrelevant patterns or noise.Key intellectual contributions of this project will be (1) a novel framework of representation learning that provides robust representations from large amounts of unlabeled data and relatively small amounts of labeled data, and (2) theoretical and algorithmic advances for inference, learning, and related optimization problems in representation learning for large-scale, complex sensory information processing.This work will serve as a catalyst leading to applications, such as multimedia processing and search, medical image processing, speech recognition, and autonomous navigation. The results will be disseminated through publications and free software.