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用于自动驾驶的机器学习:近期的进步和未来的挑战

此演讲使用中文 (This will be presented in Chinese)

Li Erran Li (Scale | Columbia University)
11:1511:55 Thursday, April 12, 2018
实施人工智能 (Implementing AI), 模型与方法 (Models and Methods)
Location: 多功能厅3A+B (Function Room 3A+B)
Secondary topics:  增强学习(Reinforcement Learning), 运输与物流 (Transportation and Logistics)

必要预备知识 (Prerequisite Knowledge)

有深度学习和增强学习的基本知识

您将学到什么 (What you'll learn)

理解用于自动驾驶的机器学习的方法

描述 (Description)

深度增强学习已经让人工智能体在很多挑战性的领域可以取得超越人类的表现,例如玩Atari的游戏以及下围棋。这一方法还具有能显著地推进自动驾驶的潜力。Erran Li将会讨论近期在模仿学习方面(例如infoGAIL)、策略梯度法和层次增强学习(例如option-critic架构)等方面的进步,以及它们在自动驾驶方面的应用。Erran接着还会介绍在这个领域需要关注的剩余的挑战。

Photo of Li Erran Li

Li Erran Li

Scale | Columbia University

Li Erran Li is the head of machine learning at Scale and an adjunct professor at Columbia University. Previously, he was chief scientist at Pony.ai. Before that, he was with the perception team at Uber ATG and machine learning platform team at Uber where he worked on deep learning for autonomous driving, led the machine learning platform team technically, and drove strategy for company-wide artificial intelligence initiatives. He started his career at Bell Labs. Li’s current research interests are machine learning, computer vision, learning-based robotics, and their application to autonomous driving. He has a PhD from the computer science department at Cornell University. He’s an IEEE Fellow and an ACM Fellow.