摘要翻译:
我们提出了一个快速的,完全可参数化的卷积神经网络变体的GPU实现。我们的特征提取器既不是精心设计的,也不是预先布线的,而是在监督下学习的。在对象分类(NORB、CIFAR10)和手写数字识别(MNIST)的基准测试中,我们的深度分层体系结构取得了最好的结果,错误率分别为2.53%、19.51%和0.35%。通过简单反向传播训练的深网比浅网性能更好。学习出奇地迅速。NORB在五个时代内被完全训练。MNIST的测试错误率在1,3和17个历元后分别下降到2.42%,0.97%和0.48%。
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英文标题:
《High-Performance Neural Networks for Visual Object Classification》
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作者:
Dan C. Cire\c{s}an, Ueli Meier, Jonathan Masci, Luca M. Gambardella
and J\"urgen Schmidhuber
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最新提交年份:
2011
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分类信息:
一级分类:Computer Science 计算机科学
二级分类:Artificial Intelligence 人工智能
分类描述:Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域,除了视觉、机器人、机器学习、多智能体系统以及计算和语言(自然语言处理),这些领域有独立的学科领域。特别地,包括专家系统,定理证明(尽管这可能与计算机科学中的逻辑重叠),知识表示,规划,和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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一级分类:Computer Science 计算机科学
二级分类:Neural and Evolutionary Computing 神经与进化计算
分类描述:Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
涵盖神经网络,连接主义,遗传算法,人工生命,自适应行为。大致包括ACM学科类C.1.3、I.2.6、I.5中的一些材料。
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英文摘要:
We present a fast, fully parameterizable GPU implementation of Convolutional Neural Network variants. Our feature extractors are neither carefully designed nor pre-wired, but rather learned in a supervised way. Our deep hierarchical architectures achieve the best published results on benchmarks for object classification (NORB, CIFAR10) and handwritten digit recognition (MNIST), with error rates of 2.53%, 19.51%, 0.35%, respectively. Deep nets trained by simple back-propagation perform better than more shallow ones. Learning is surprisingly rapid. NORB is completely trained within five epochs. Test error rates on MNIST drop to 2.42%, 0.97% and 0.48% after 1, 3 and 17 epochs, respectively.
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PDF链接:
https://arxiv.org/pdf/1102.0183