Deep Learning and Representation Learning for Sequential Data

计算的普及导致了比以往任何时候都多的数据的产生和存储。机器学习试图将这些海量数据转化为智能系统，以识别模式并做出决策。它彻底改变了计算机视觉、计算神经科学、生物学和社会科学等领域。但是，当面对越来越复杂的数据时，机器如何知道哪些部分是相关的？它如何将数百万个组件组织成有组织的单元，以供决策之用？机器学习的最新发展，被称为“深度学习”，通过展示越来越抽象的特征层如何在没有人类指导的情况下提取信息数据表示来回答这些问题。但到目前为止，该领域的重点是静态数据，而不是动态数据：特别是视觉推理背景下的图像。然而，我们在机器学习应用程序中遇到的许多真实世界的数据都具有时间依赖性，这些依赖性通常会在很大的时间尺度上延伸。例如人类或机器人的运动、气候数据、音频（例如音乐或语音）和金融。建模序列是具有挑战性的：即使是最复杂的技术也无法学习长期结构。序列增加了计算的要求，因为我们试图解开更复杂的解释因素的基础data.I提出了一个研究计划，面对这些挑战，通过发现的算法和架构，学习表示从序列。我还希望通过跨学科合作，扩大深度学习在学术界之外的应用，并提高其相关性，这将影响生物学、娱乐和金融等领域。我将培养能够满足行业对数据科学家日益增长的需求的高素质人才。

The pervasiveness of computing has resulted in the production and storage of more data than ever before. Machine learning seeks to transform this deluge of data into intelligent systems that identify patterns and make decisions. It has revolutionized fields as diverse as computer vision, computational neuroscience, biology and the social sciences. But when faced with data that is increasingly complex, how does a machine know which parts are relevant? How does it structure the millions of components into organized units on which it can base decisions?Recent developments in machine learning, known as "Deep Learning", have answered these questions by showing how increasingly abstract layers of features can extract informative data representations without human guidance. But to-date, the field has focused on static as opposed to dynamic data: particularly images in the context of visual reasoning. Much of the real-world data we encounter in machine learning applications, however, has temporal dependencies that often extend over large time scales. Examples are human or robot motion, climate data, audio (e.g. music or speech), and finance. Modeling sequences is challenging: even the most sophisticated techniques fail to learn long-term structure. Sequences increase computational requirements as we attempt to untangle more complex explanatory factors underlying the data.I propose a research program that confronts these challenges through the discovery of algorithms and architectures that learn representations from sequences. I also aim to widen the adoption and increase the relevance of Deep Learning outside of academia through interdisciplinary collaborations that will impact fields such as biology, entertainment, and finance. I will train high-quality personnel who can satiate industry's growing demand for data scientists.