EAGER: Toward Scalable Life-long Representation Learning

机器学习是解决人工智能和数据挖掘问题的有力工具。然而，它的成功关键依赖于数据的良好特征表示；因此，特征构建问题提出了一个根本性的挑战。近年来，表征学习已经成为一种从数据中学习有用特征表征的有前途的方法。然而，目前最先进的方法在构建能够学习和与复杂环境和大量感官输入交互的智能代理方面仍然受到限制。具体来说，现有的大多数方法不能很好地扩展到大规模数据。该项目的目标是通过制定一个新的框架来填补这一空白，该框架可以有效地从复杂环境中学习表征并扩展到大数据。具体来说，我们提出了从大规模数据中学习鲁棒表示的新方法：(1)控制特征表示的复杂性；(2)在存在大量不相关模式或噪声的情况下自适应建模相关模式。本项目的关键智力贡献将是：(1)一个新的表示学习框架，从大量未标记数据和相对少量的标记数据中提供鲁棒表示，以及(2)在大规模复杂感官信息处理的表示学习中进行推理、学习和相关优化问题的理论和算法进步。这项工作将成为引领应用的催化剂，如多媒体处理和搜索、医学图像处理、语音识别和自主导航。结果将通过出版物和自由软件传播。

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.