Yann LeCun是纽约大学Courant数学科学研究所的银教授，也是Facebook的副总裁兼首席人工智能科学家。他获得了电子技术与电子技术高级学院（ESIEE）颁发的电子工程硕士学位，以及皮埃尔与玛丽·居里大学的计算机科学博士学位。
他的荣誉包括美国国家工程院院士；来自IPN Mexico和École Polytechnique Fédérale de Lausanne（EPFL）的Honoris Causa博士学位；宾夕法尼亚大学彭德奖；荷斯特奖章，埃因霍温工业大学和飞利浦实验室；诺基亚贝尔实验室香农灯具奖；IEEE PAMI杰出研究员奖；以及IEEE神经网络先锋奖。他还被《连线》杂志评选为“The Wired 100-2016最具影响力人物”及其“25位正在创造商业未来的天才”。LeCun是纽约大学数据科学中心的创始主任，也是CIFAR机器与大脑学习项目的联合主任（与Yoshua Bengio）。LeCun还是人工智能合作组织的联合创始人和前董事会成员，该组织由研究人工智能社会后果的公司和非营利组织组成。 卷积神经网络：在20世纪80年代，LeCun开发了卷积神经网络，这是该领域的一项基本原理，除其他优势外，它对提高深度学习的效率至关重要。20世纪80年代末，在多伦多大学和贝尔实验室工作时，LeCun是第一个在手写数字图像上训练卷积神经网络系统的人。如今，卷积神经网络是计算机视觉、语音识别、语音合成、图像合成和自然语言处理的行业标准。它们被用于各种各样的应用，包括自动驾驶、医学图像分析、声控助手和信息过滤。 改进反向传播算法：LeCun提出了反向传播算法的早期版本，并基于变分原理对其进行了清晰的推导。他加快反向传播算法的工作包括描述两种简单的方法来加快学习时间。 拓宽了神经网络的视野：LeCun还被认为为神经网络开发了更广阔的视野，将其作为一种广泛任务的计算模型，在早期工作中引入了许多现在是人工智能基础的概念。例如，在识别图像的背景下，他研究了如何在神经网络中学习层次特征表示，这一概念现在被常规用于许多识别任务。他与Léon Bottou一起提出了一个想法，用于每一个现代深度学习软件，即学习系统可以构建为复杂的模块网络，通过自动区分进行反向传播。他们还提出了可以操作结构化数据（如图）的深度学习架构。 Convolutional neural networks: In the 1980s, LeCun developed convolutional neural networks, a foundational principle in the field, which, among other advantages, have been essential in making deep learning more efficient. In the late 1980s, while working at the University of Toronto and Bell Labs, LeCun was the first to train a convolutional neural network system on images of handwritten digits. Today, convolutional neural networks are an industry standard in computer vision, as well as in speech recognition, speech synthesis, image synthesis, and natural language processing. They are used in a wide variety of applications, including autonomous driving, medical image analysis, voice-activated assistants, and information filtering. Improving backpropagation algorithms: LeCun proposed an early version of the backpropagation algorithm (backprop), and gave a clean derivation of it based on variational principles. His work to speed up backpropagation algorithms included describing two simple methods to accelerate learning time. Broadening the vision of neural networks: LeCun is also credited with developing a broader vision for neural networks as a computational model for a wide range of tasks, introducing in early work a number of concepts now fundamental in AI. For example, in the context of recognizing images, he studied how hierarchical feature representation can be learned in neural networks—a concept that is now routinely used in many recognition tasks. Together with Léon Bottou, he proposed the idea, used in every modern deep learning software, that learning systems can be built as complex networks of modules where backpropagation is performed through automatic differentiation. They also proposed deep learning architectures that can manipulate structured data, such as graphs. |
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