Gradient-Based Learning for Object Detection, Segmentation and RecognitionYann LecunPatrick HaffnerY Bengio
Gradient-Based Learning Applied to Document Recognition阅读笔记-LeNet模型,程序员大本营,技术文章内容聚合第一站。
One of the most crucial hyperparameters is the learning rate which controls the speed and direction of updates to the weights during training. We proposed an adaptive scheduler called Gradient-based Learning Rate scheduler(GLR) that significantly reduces the tuning effort thanks to a single user-...
Yann LeCun,生于1960年,是一位机器学习、计算机视觉、机器人、计算神经科学领域的计算机科学家。他被大家所熟知的是在非光学字符识别和利用卷积神经网络(CNN)实现计算视觉方面的工作,是CNN之父。他也是DjVu图像压缩技术的主要创造者之一。他与Léon Bottou.共同开发了L
1.论文:Gradient-Based Learning Applied to Document Recognition 2.网络结构: LeNet包括7个layers(不包括Input),Fig 1中的C、S和F分别指卷积层、下采样层(池化层)和全连接层,其后所跟随的数字1-6指所在层的索引位置。例如,S2意为在网络结构中索引为2的位置的下采样层。
CNN经典论文学习第一篇,卷积神经网络开山鼻祖,经典的手写体识别论文——LeNet:《Gradient-Based Learning Applied to Document Recognition》,作者包括深度学习三大巨头之一Yann Lecun,花书《深度学习》作者之一Yoshua Bengio。 原文篇幅很长,选择记录其中最重要的介绍CNN网络结构的第二章的A和B部分。
(1998) LeCun et al. Proceedings of the IEEE. Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient-based learning technique. Given an appropriate network architecture, gradient-based l
Gradient-Based Learning Applied to Document Recognition ´ YANN LECUN, MEMBER, IEEE, LEON BOTTOU, YOSHUA BENGIO, AND PATRICK HAFFNER Invited Paper Multilayer neural networks trained with the back-propagation NN Neural network. algorithm constitute the best example of a successful gradient- OCR Optica...
A new learning paradigm, called Graph Transformer Networks (GTN), allows such multi-module systems to be trained globally using Gradient-Based methods so as to minimize an overall performance measure. 现实生活中的文档识别系统是由多个模块组成的,包括字段提取、分割、识别和语言建模。一种新的学习范式...
Yann LeCun,生于1960年,是一位机器学习、计算机视觉、机器人、计算神经科学领域的计算机科学家。他被大家所熟知的是在非光学字符识别和利用卷积神经网络(CNN)实现计算视觉方面的工作,是CNN之父。他也是DjVu图像压缩技术的主要创造者之一。他与Léon Bottou.共同开发了Lush编程语言。(from Wikipedia)特别说明 这篇文章...