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\u8fd9\u4e2a\u56de\u7b54\u7684\u5999\u5904\u5728\u4e8e\uff1a\u5b83\u628a\u72ed\u4e49\u76f8\u5bf9\u8bba\u91cc\u4e00\u4ef6\u76f8\u5f53\"\u53cd\u76f4\u89c9\"\u7684\u4e8b\uff0c\u7ffb\u8bd1\u6210\u4e00\u4e2a\u76f8\u5f53\"\u53cd\u81ea\u604b\"\u7684\u63d0\u9192\u2014\u2014\u4e0d\u662f\u5b87\u5b99\u6545\u610f\u4e3a\u96be\u4f60\uff0c\u800c\u662f\u4f60\u592a\u76f8\u4fe1\u81ea\u5df1\u90a3\u5957\u52a0\u6cd5\u3002

\u4e00\u3001\u76f8\u5bf9\u8bba

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u = (v + u\u2032) / (1 + vu\u2032/c\u00b2)

\u5b83\u50cf\u4e00\u4f4d\u4e0d\u901a\u4eba\u60c5\u7684\u4f1a\u8ba1\uff1a\u4f60\u8bf4\"\u4e24\u4efd\u5c31\u662f\u4e24\u4efd\"\uff0c\u4ed6\u504f\u8bf4\"\u8981\u770b\u4f60\u4e24\u4efd\u662f\u600e\u4e48\u7b97\u7684\"\u3002\u4f46\u4f60\u82e5\u628a\"\u901f\u5ea6\"\u6362\u6210\"\u5feb\u5ea6\"\uff08 rapidity \uff09\uff0c\u90a3\u4f4d\u4f1a\u8ba1\u7acb\u523b\u53d8\u5f97\u901a\u8fbe\uff1a**\u5feb\u5ea6\u5728\u5171\u7ebf\u60c5\u5f62\u4e0b\u53ef\u4ee5\u76f4\u63a5\u76f8\u52a0\uff0c\u800c\u901f\u5ea6\u53ea\u662f\u5feb\u5ea6\u7684\u67d0\u79cd\"\u5448\u73b0\"**\u3002

\u4e25\u683c\u4e00\u70b9\u6765\u8bf4\uff0c\u5feb\u5ea6 w \u548c\u901f\u5ea6 v \u7684\u5173\u7cfb\u5e38\u5199\u4f5c \u03b2 = v/c = tanh(w)\u3002\u4e8e\u662f\u4e24\u6b21\"\u5feb\u5ea6\u76f8\u52a0\"w = w\u2081 + w\u2082\uff0c\u7ecf\u7531 tanh \u8fd9\u4e2a\"\u4f1a\u9971\u548c\u7684\u51fd\u6570\"\u4e00\u6295\u5f71\uff0c\u4fbf\u5f97\u5230\u4e0a\u9762\u90a3\u6761\u901f\u5ea6\u5408\u6210\u516c\u5f0f\u3002

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\u8fd9\u91cc\u6211\u6597\u80c6\u505a\u70b9\u5c0f\u7684\u6279\u6ce8\uff1a\u7b54\u4e3b\u7528 tan(90\u00b0) \u4e4b\u7c7b\u6765\u8bb2\u6781\u9650\uff0c\u662f\u4e3a\u4e86\u53d9\u4e8b\u987a\u53e3\uff1b\u4f46\u76f8\u5bf9\u8bba\u66f4\u81ea\u7136\u7684\u662f\u53cc\u66f2\u51fd\u6570 tanh \uff0c\u5b83\u7684\u6781\u9650\u662f 1 \uff0c\u6240\u4ee5 v \u7684\u6781\u9650\u662f c \u3002\u8bb2\u6545\u4e8b\u53ef\u4ee5\u7528\"\u5938\u5f20\"\uff0c\u4f46\u8bfb\u8005\u5fc3\u91cc\u6700\u597d\u8981\u7559\u4e00\u4e2a\"\u6821\u5bf9\u5458\"\uff1a\u6545\u4e8b\u7684\u5938\u5f20\u7528\u6765\u6293\u76f4\u89c9\uff0c\u4e0d\u7528\u6765\u66ff\u4ee3\u6570\u5b66\u3002

\u901f\u5ea6\u4e0d\u613f\u610f\u8ba9\u4eba\u505a\u7ebf\u6027\u76f8\u52a0\uff0c\u80cc\u540e\u5e76\u4e0d\u53ea\u662f\"\u51fd\u6570\u6362\u4e86\u4e2a\u76ae\"\uff0c\u800c\u662f\"\u6362\u4e86\u51e0\u4f55\u7684\u4e3b\u5fc3\u9aa8\"\u3002

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\u4e8c\u3001\u6d1e\u7a74\u4e4b\u5bd3

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\u4e09\u3001\u975e\u6052\u9053

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\u4e8c\u3001\u95ee\u9898
1. \u6280\u672f\u4e0e\u4ee3\u7801\u8d28\u91cf\u95ee\u9898
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2. \u67b6\u6784\u4e0e\u8bbe\u8ba1\u5c42\u9762\u7684\u5c40\u9650
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\u7eff\u6ce1\u6ce1\uff1aR25haXhFdXk2NTUzNQ==" }, { "author": { "url": "member/miracleyao", "name": "miracleyao", "avatar": "https://cdn.v2ex.com/avatar/913c/4d10/155970_large.png?m=1729838514" }, "url": "t/1184047", "title": "\u4ea4\u6613\u65e5\u5fd7", "id": "t/1184047", "date_published": "2026-01-08T07:49:56+00:00", "content_html": "

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\n" }, { "author": { "url": "member/rosebush373", "name": "rosebush373", "avatar": "https://cdn.v2ex.com/gravatar/b085f045d8dc7ecf659191d1b6a2356a?s=73&d=retro" }, "url": "t/1182759", "date_modified": "2026-01-03T03:51:20+00:00", "content_html": "", "date_published": "2026-01-03T01:39:50+00:00", "title": "\u5931\u4e1a\u5728\u5bb6 2.5 \u5e74\u4e86\uff0c\u5927\u6982\u7387\u65e0\u6cd5\u4e0a\u73ed\u4e86\uff0c\u8f9e\u804c\u7684\u65f6\u5019\u5c31\u5df2\u7ecf\u60f3\u597d\u4e86\u81ea\u5df1\u7684\u672a\u6765", "id": "t/1182759" }, { "author": { "url": "member/Wenen2020", "name": "Wenen2020", "avatar": "https://cdn.v2ex.com/avatar/6bb2/74bf/531284_large.png?m=1767137722" }, "url": "t/1182642", "title": "\u6211\u771f\u7684\u4e0d\u662f\u8981\u641e\u5bf9\u7acb\uff0c\u4f46\u8fd9\u91cc\u7684\u4ea4\u6d41\u6c1b\u56f4\u6bd4\u5c0f\u7ea2\u4e66\u597d\u592a\u591a\u4e86", "id": "t/1182642", "date_published": "2026-01-01T23:00:43+00:00", "content_html": "\u6211\u4e00\u76f4\u90fd\u627f\u8ba4\u6211\u7684\u4ea7\u54c1 [\u5999\u7b14\u751f\u82b1]( https://wordflower.cn/) \u4ed6\u4e0d\u662f\u5b8c\u7f8e\u7684\uff0c\u751a\u81f3\u8fd8\u6709\u5f88\u591a bug \uff0c\u4f46\u771f\u7684\u5f88\u96be\u63a5\u53d7\u5c0f\u7ea2\u4e66\u90a3\u4e9b\u8fde\u7528\u90fd\u4e0d\u7528\u5c31\u5f00\u55b7\u7684\u4eba\uff0c\u771f\u7684\u504f\u6267\u53c8\u81ea\u5927\u3002
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\n", "date_published": "2026-01-01T12:58:04+00:00", "title": "\u54c8\u54c8\uff0c\u4f30\u8ba1\u662f\u8001\u4e86\u5427", "id": "t/1182612" }, { "author": { "url": "member/winxberg", "name": "winxberg", "avatar": "https://cdn.v2ex.com/avatar/663b/12c6/200148_large.png?m=1748940875" }, "url": "t/1182308", "date_modified": "2025-12-31T01:45:40+00:00", "content_html": "

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\u613f\u4e2d\u56fd\u9752\u5e74\u90fd\u6446\u8131\u51b7\u6c14\uff0c\u53ea\u662f\u5411\u4e0a\u8d70\uff0c\u4e0d\u5fc5\u542c\u81ea\u66b4\u81ea\u5f03\u8005\u6d41\u7684\u8bdd\u3002\u80fd\u505a\u4e8b\u7684\u505a\u4e8b\uff0c\u80fd\u53d1\u58f0\u7684\u53d1\u58f0\u3002\u6709\u4e00\u5206\u70ed\uff0c\u53d1\u4e00\u5206\u5149\uff0c\u5c31\u4ee4\u8424\u706b\u4e00\u822c\uff0c\u4e5f\u53ef\u4ee5\u5728\u9ed1\u6697\u91cc\u53d1\u4e00\u70b9\u5149\uff0c\u4e0d\u5fc5\u7b49\u5019\u70ac\u706b\u3002

\n" }, { "author": { "url": "member/200dev", "name": "200dev", "avatar": "https://cdn.v2ex.com/avatar/739c/bdcc/303973_large.png?m=1772288634" }, "url": "t/1180856", "title": "AI \u5386\u53f2 top20 \u8bba\u6587", "id": "t/1180856", "date_published": "2025-12-24T03:02:46+00:00", "content_html": "\u4ee5\u4e0b\u662f AI \u5386\u53f2\u4e0a\u5f15\u7528\u6b21\u6570\u6700\u591a\u7684 20 \u7bc7\u673a\u5668\u5b66\u4e60\u8bba\u6587\uff08\u57fa\u4e8e\u53ef\u9760\u6765\u6e90\u7684\u6700\u65b0\u7edf\u8ba1\uff0c\u6570\u636e\u6765\u6e90\u4e8e Google Scholar \uff0c\u91c7\u96c6\u4e8e 2025 \u5e74 12 \u6708\u5de6\u53f3\uff0c\u53ef\u80fd\u56e0\u65f6\u95f4\u7565\u6709\u53d8\u52a8\uff09\u3002\u6211\u5148\u6536\u96c6\u4e86 top50 \u5de6\u53f3\u7684\u77e5\u540d\u8bba\u6587\u5217\u8868\uff0c\u7136\u540e\u901a\u8fc7\u641c\u7d22\u66f4\u65b0\u4e86\u5b83\u4eec\u7684\u5f15\u7528\u6b21\u6570\uff0c\u5e76\u91cd\u65b0\u6392\u5e8f\u53d6 top20 \uff0c\u4ee5\u786e\u4fdd\u51c6\u786e\u6027\u3002\u6392\u540d\u548c\u6570\u636e\u6765\u6e90\u4e8e\u7f51\u9875\u6765\u6e90\u5982 Doradolist \u548c Nature Index \u7b49\u3002

\u6bcf\u4e2a\u8bba\u6587\u5305\u62ec\u7b80\u79f0\uff08\u5982\u679c\u9002\u7528\uff09\u3001\u4f5c\u8005\u3001\u5e74\u4efd\u3001\u4f1a\u8bae/\u671f\u520a\u3001\u6700\u65b0\u5f15\u7528\u6b21\u6570\u3001PDF \u94fe\u63a5\uff0c\u4ee5\u53ca\u8003\u8bc1\u94fe\u63a5\uff08 Google Scholar \u641c\u7d22\u94fe\u63a5\uff0c\u7528\u4e8e\u9a8c\u8bc1\u6700\u65b0\u6570\u636e\uff09\u3002

1. **Deep Residual Learning for Image Recognition** (\u7b80\u79f0: ResNet)
\u4f5c\u8005: Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
\u5e74\u4efd: 2016
\u4f1a\u8bae/\u671f\u520a: Computer Vision and Pattern Recognition (CVPR)
\u5f15\u7528\u6b21\u6570: 151,914
PDF \u94fe\u63a5: https://arxiv.org/pdf/1512.03385.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Deep Residual Learning for Image Recognition\" \"Kaiming He\" \"Xiangyu Zhang\" \"Shaoqing Ren\" \"Jian Sun\"

2. **Adam: A Method for Stochastic Optimization** (\u7b80\u79f0: Adam)
\u4f5c\u8005: Diederik P. Kingma, Jimmy Lei Ba
\u5e74\u4efd: 2014
\u4f1a\u8bae/\u671f\u520a: Proceedings of the 3rd International Conference on Learning Representations (ICLR)
\u5f15\u7528\u6b21\u6570: 135,894
PDF \u94fe\u63a5: https://arxiv.org/pdf/1412.6980.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Adam: A Method for Stochastic Optimization\" \"Diederik P. Kingma\" \"Jimmy Lei Ba\"

3. **ImageNet Classification with Deep Convolutional Neural Networks** (\u7b80\u79f0: AlexNet)
\u4f5c\u8005: Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton
\u5e74\u4efd: 2012
\u4f1a\u8bae/\u671f\u520a: Advances in neural information processing systems
\u5f15\u7528\u6b21\u6570: 126,795
PDF \u94fe\u63a5: https://proceedings.neurips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"ImageNet Classification with Deep Convolutional Neural Networks\" \"Alex Krizhevsky\" \"Ilya Sutskever\" \"Geoffrey E Hinton\"

4. **Random Forests** (\u7b80\u79f0: Random Forest)
\u4f5c\u8005: Leo Breiman
\u5e74\u4efd: 2001
\u4f1a\u8bae/\u671f\u520a: Machine learning 45
\u5f15\u7528\u6b21\u6570: 103,134
PDF \u94fe\u63a5: https://www.stat.berkeley.edu/~breiman/randomforest2001.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Random Forests\" \"Leo Breiman\"

5. **Very Deep Convolutional Networks for Large-Scale Image Recognition** (\u7b80\u79f0: VGG)
\u4f5c\u8005: Karen Simonyan, Andrew Zisserman
\u5e74\u4efd: 2015
\u4f1a\u8bae/\u671f\u520a: International Conference on Learning Representations (ICLR)
\u5f15\u7528\u6b21\u6570: 95,218
PDF \u94fe\u63a5: https://arxiv.org/pdf/1409.1556.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Very Deep Convolutional Networks for Large-Scale Image Recognition\" \"Karen Simonyan\" \"Andrew Zisserman\"

6. **Scikit-learn: Machine Learning in Python** (\u7b80\u79f0: Scikit-Learn)
\u4f5c\u8005: Fabian Pedregosa, Ga\u00ebl Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, Edouard Duchesnay
\u5e74\u4efd: 2011
\u4f1a\u8bae/\u671f\u520a: The Journal of machine Learning research
\u5f15\u7528\u6b21\u6570: 68,584
PDF \u94fe\u63a5: https://www.jmlr.org/papers/volume12/pedregosa11a/pedregosa11a.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Scikit-learn: Machine Learning in Python\" \"Fabian Pedregosa\" \"Ga\u00ebl Varoquaux\" \"Alexandre Gramfort\"

7. **Deep Learning** (\u7b80\u79f0: \u65e0\u6807\u51c6\u7b80\u79f0\uff0c\u5e38\u79f0\u201cDeep Learning \u7efc\u8ff0\u201d)
\u4f5c\u8005: Yann LeCun, Yoshua Bengio, Geoffrey Hinton
\u5e74\u4efd: 2015
\u4f1a\u8bae/\u671f\u520a: Nature
\u5f15\u7528\u6b21\u6570: 60,460
PDF \u94fe\u63a5: https://www.researchgate.net/publication/277411157_Deep_learning
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Deep Learning\" \"Yann LeCun\" \"Yoshua Bengio\" \"Geoffrey Hinton\"

8. **Support-Vector Networks** (\u7b80\u79f0: SVM)
\u4f5c\u8005: Corinna Cortes, Vladimir Vapnik
\u5e74\u4efd: 1995
\u4f1a\u8bae/\u671f\u520a: Machine learning 20
\u5f15\u7528\u6b21\u6570: 58,015
PDF \u94fe\u63a5: https://web.engr.oregonstate.edu/~huanlian/teaching/ML/2018spring/extra/svn-1995.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Support-Vector Networks\" \"Corinna Cortes\" \"Vladimir Vapnik\"

9. **Generative Adversarial Nets** (\u7b80\u79f0: GAN)
\u4f5c\u8005: Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio
\u5e74\u4efd: 2014
\u4f1a\u8bae/\u671f\u520a: Advances in neural information processing systems 27
\u5f15\u7528\u6b21\u6570: 55,028
PDF \u94fe\u63a5: https://arxiv.org/pdf/1406.2661.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Generative Adversarial Nets\" \"Ian Goodfellow\" \"Jean Pouget-Abadie\" \"Mehdi Mirza\"

10. **Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks** (\u7b80\u79f0: Faster R-CNN)
\u4f5c\u8005: Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun
\u5e74\u4efd: 2015
\u4f1a\u8bae/\u671f\u520a: Advances in neural information processing systems
\u5f15\u7528\u6b21\u6570: 54,317
PDF \u94fe\u63a5: https://proceedings.neurips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks\" \"Shaoqing Ren\" \"Kaiming He\" \"Ross Girshick\" \"Jian Sun\"

11. **LIBSVM: A Library for Support Vector Machines** (\u7b80\u79f0: LIBSVM)
\u4f5c\u8005: Chih-Chung Chang, Chih-Jen Lin
\u5e74\u4efd: 2011
\u4f1a\u8bae/\u671f\u520a: ACM Transactions on Intelligent Systems and Technology (TIST)
\u5f15\u7528\u6b21\u6570: 53,118
PDF \u94fe\u63a5: https://www.csie.ntu.edu.tw/~cjlin/papers/libsvm.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"LIBSVM: A Library for Support Vector Machines\" \"Chih-Chung Chang\" \"Chih-Jen Lin\"

12. **Gradient-Based Learning Applied to Document Recognition** (\u7b80\u79f0: LeNet)
\u4f5c\u8005: Yann LeCun, L\u00e9on Bottou, Yoshua Bengio, Patrick Haffner
\u5e74\u4efd: 1998
\u4f1a\u8bae/\u671f\u520a: Proceedings of the IEEE
\u5f15\u7528\u6b21\u6570: 52,044
PDF \u94fe\u63a5: http://vision.stanford.edu/cs598_spring07/papers/Lecun98.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Gradient-Based Learning Applied to Document Recognition\" \"Yann LeCun\" \"L\u00e9on Bottou\" \"Yoshua Bengio\" \"Patrick Haffner\"

13. **ImageNet: A Large-Scale Hierarchical Image Database** (\u7b80\u79f0: ImageNet)
\u4f5c\u8005: Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei
\u5e74\u4efd: 2009
\u4f1a\u8bae/\u671f\u520a: Proceedings of the IEEE conference on computer vision and pattern recognition
\u5f15\u7528\u6b21\u6570: 49,614
PDF \u94fe\u63a5: https://www.image-net.org/static_files/papers/imagenet_cvpr09.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"ImageNet: A Large-Scale Hierarchical Image Database\" \"Jia Deng\" \"Wei Dong\" \"Richard Socher\"

14. **Going Deeper with Convolutions** (\u7b80\u79f0: Inception \u6216 GoogLeNet)
\u4f5c\u8005: Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich
\u5e74\u4efd: 2015
\u4f1a\u8bae/\u671f\u520a: Proceedings of the IEEE conference on computer vision and pattern recognition
\u5f15\u7528\u6b21\u6570: 47,812
PDF \u94fe\u63a5: https://arxiv.org/pdf/1409.4842.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Going Deeper with Convolutions\" \"Christian Szegedy\" \"Wei Liu\" \"Yangqing Jia\"

15. **Latent Dirichlet Allocation** (\u7b80\u79f0: LDA)
\u4f5c\u8005: David M. Blei, Andrew Y. Ng, Michael I. Jordan
\u5e74\u4efd: 2003
\u4f1a\u8bae/\u671f\u520a: Journal of Machine Learning Research 3
\u5f15\u7528\u6b21\u6570: 45,728
PDF \u94fe\u63a5: https://www.jmlr.org/papers/volume3/blei03a/blei03a.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Latent Dirichlet Allocation\" \"David M. Blei\" \"Andrew Y. Ng\" \"Michael I. Jordan\"

16. **Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift** (\u7b80\u79f0: BatchNorm)
\u4f5c\u8005: Sergey Ioffe, Christian Szegedy
\u5e74\u4efd: 2015
\u4f1a\u8bae/\u671f\u520a: International conference on machine learning
\u5f15\u7528\u6b21\u6570: 44,295
PDF \u94fe\u63a5: https://arxiv.org/pdf/1502.03167.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift\" \"Sergey Ioffe\" \"Christian Szegedy\"

17. **TensorFlow: A System for Large-Scale Machine Learning** (\u7b80\u79f0: TensorFlow)
\u4f5c\u8005: Mart\u00edn Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, Manjunath Kudlur, Josh Levenberg, Rajat Monga, Sherry Moore, Derek G Murray, Benoit Steiner, Paul Tucker, Vijay Vasudevan, Pete Warden, Martin Wicke, Yuan Yu, Xiaoqiang Zheng
\u5e74\u4efd: 2016
\u4f1a\u8bae/\u671f\u520a: 12th USENIX symposium on operating systems design and implementation (OSDI)
\u5f15\u7528\u6b21\u6570: 43,523
PDF \u94fe\u63a5: https://www.usenix.org/system/files/conference/osdi16/osdi16-abadi.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"TensorFlow: A System for Large-Scale Machine Learning\" \"Mart\u00edn Abadi\" \"Paul Barham\" \"Jianmin Chen\"

18. **Dropout: A Simple Way to Prevent Neural Networks from Overfitting** (\u7b80\u79f0: Dropout)
\u4f5c\u8005: Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov
\u5e74\u4efd: 2014
\u4f1a\u8bae/\u671f\u520a: The journal of machine learning research
\u5f15\u7528\u6b21\u6570: 40,815
PDF \u94fe\u63a5: https://www.cs.toronto.edu/~hinton/absps/JMLRdropout.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"Dropout: A Simple Way to Prevent Neural Networks from Overfitting\" \"Nitish Srivastava\" \"Geoffrey Hinton\" \"Alex Krizhevsky\"

19. **ImageNet Large Scale Visual Recognition Challenge** (\u7b80\u79f0: ILSVRC)
\u4f5c\u8005: Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C Berg, Li Fei-Fei
\u5e74\u4efd: 2015
\u4f1a\u8bae/\u671f\u520a: International journal of computer vision
\u5f15\u7528\u6b21\u6570: 35,821
PDF \u94fe\u63a5: https://arxiv.org/pdf/1409.0575.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"ImageNet Large Scale Visual Recognition Challenge\" \"Olga Russakovsky\" \"Jia Deng\" \"Hao Su\"

20. **MapReduce: Simplified Data Processing on Large Clusters** (\u7b80\u79f0: MapReduce)
\u4f5c\u8005: Jeffrey Dean, Sanjay Ghemawat
\u5e74\u4efd: 2008
\u4f1a\u8bae/\u671f\u520a: Communications of the ACM 51
\u5f15\u7528\u6b21\u6570: 34,813
PDF \u94fe\u63a5: https://research.google.com/archive/mapreduce-osdi04.pdf
\u8003\u8bc1\u94fe\u63a5: https://scholar.google.com/scholar?q=\"MapReduce: Simplified Data Processing on Large Clusters\" \"Jeffrey Dean\" \"Sanjay Ghemawat\"

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