走向通用人工智能，到底是先“理解智能”还是先“制造智能”？ 近期，在IJAC优先在线发表的几篇论文中， 北京大学黄铁军 教授的综述成为近两月优先发表论文中的亮点之一。 黄铁军教授在这篇综述中提出的“仿真主义”（Imitationalism）可谓人工智能方法中第五的流派！ （熟悉人工智能的小伙伴可能都了解：人工智能的基本思想大致可划分为四个流派：符号主义、链接主义、行为主义和统计主义） 文章不仅阐述了如何打破走向通用人工智能的研究僵局、探索研制类脑神经计算机的新思路，还详细描述了类脑神经计算机三个重要技术层次和国内外研究进展。与IJAC 4月优先在线发表的麻省理工美国人文科学院院士Tomaso Poggio 教授关于深度学习机理的文章一样，本篇综述绝对是 行业宝典 ！且此综述已在Twitter被相关学者转发数次。 通用人工智能（Artificial General Intelligence, AGI）指可以像人一样完成各种智能任务的类人智能机器，AGI又称为Srong AI或fullAI. 为了获得通用人工智能，到底是先“理解智能”？ （即理解意识现象和功能背后的发生机理） 还是先“制造智能” （即研制类似人脑的具有自我意识的智能机器）？ ——这是一个问题！ 传统人工智能的思维定式是在“理解智能”机理的基础上设计制造人工智能系统，即先理解智能再模仿智能。然而人类对自身智能的认识还处在初级阶段，在对人类智能的理解还极其有限，“理解智能”这个终极性问题到底数十年还是数百年亦或数千年才能解决？都还是未知数。因此，把“制造智能”寄希望于“理解智能”，实质上把解决问题的方案建立在解决另一个更难问题的基础上，犯了本末倒置的错误。 （图片来自于网络） 如果我们跳出传统思维的窠臼，就会发现通向通用人工智能还有一条“ 新 ” 路线——构建 类脑神经计算机 ，这里称为“仿真主义”（Imitationalism）。说这是一条新路线，是因为它反转了“理解智能”和“制造智能”的前后关系：即我们不再寻求“理解智能”的解，而是先模仿脑，即设计更先进的探测分析工具， 从结构上解析大脑 ，再利用工程技术手段“照葫芦画瓢”式地 构造仿脑装置 ，最后 通过环境刺激和交互训练“仿真大脑”，实现类人智能 。 简言之： 先结构后功能 。 （图片来自于网络） 本篇文章阐述了“先结构后功能”的类脑计算的 三层技术路线 ：结构层次模仿脑、器件层次逼近脑，智能层次超越脑；还分析了在获得通用人工智能时， 神经计算机相比传统计算机的优势、生物神经网络相比人工神经网络的优势 ，并综述了国际学者在神经计算机领域的研究进展 。 精彩内容请下载原文阅读，这正是： 人工智能一甲子，结构功能两相争； 符号系统Top down，神经网络向上攻； 进化主义玩互动，机器学习调模型； 欲破智能千古谜，先剖大脑再人工。 ——来源：《中国计算机学会通讯》2017年1月，作者黄铁军 文章免费下载信息： 【 Title 】 Imitatingthe brain with neurocomputer a “new” way towards artificial generalintelligence 【 Author 】 Tie-Jun Huang 【 Abstract 】 To achieve the artificial generalintelligence (AGI), imitate the intelligence? or imitate the brain? This is thequestion! …. To achieve AGI, a practical approach is to build the so-calledneurocomputer, which could be trained to produce autonomous intelligence andAGI. A neurocomputer imitates the biological neural network with neuromorphicdevices which emulate the bio-neurons, synapses and other essential neuralcomponents. The neurocomputer could perceive the environment via sensors andinteract with other entities via a physical body. The philosophy under the“new” approach, so-called as imitationalism in this paper, is the engineeringmethodology which has been practiced for thousands of years, and for manycases, such as the invention of the first airplane, succeeded. This papercompares the neurocomputer with the conventional computer. The major progressabout neurocomputer is also reviewed. 【 Keywords 】 Artificial general intelligence (AGI), neuromorphic computing, neurocomputer, brain-likeintelligence, imitationalism 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1082-y 5-6 月优先在线发表的计算方向论文还有如下，欢迎阅读 【 Title 】 Stability analysis of an underactuatedautonomous underwater vehicle using extended-Routh’s stability method 【 Author 】 Basant Kumar Sahu,Bidyadhar Subudhi, Madan Mohan Gupta 【 Keywords 】 Routh’s stability, extended-Routh’s stability, autonomousunderwater vehicle (AUV), underactuated system, underwater robots 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-016-0992-4 【 Title 】 Layered software patterns for data analysisin big data environment 【 Author 】 Hossam Hakeem 【 Keywords 】 Big data, data analysis, patterns layered,structure data modelling 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-016-1043-x 【 Title 】 Multi-sensor data fusion for wheelchairposition estimation with unscented Kalman filter 【 Author 】 Derradji Nada, MounirBousbia-Salah, Maamar Bettayeb 【 Keywords 】 Data fusion, unscented Kalman filter(UKF) , measurement fusion (MF), navigation, state vector fusion(SVF) , wheelchair 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1065-z 【 Title 】 Evaluation method of the gait motion based onself-organizing map using the gravity center fluctuation on the sole 【 Author 】 Koji Makino, MasahiroNakamura, Hidenori, Omori, et al. 【 Keywords 】 Gait motion, self-organizing map (SOM), rehabilitation, evaluationmethod, gravity center fluctuation (GCF) 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-016-1045-8 【 Title 】 Low-latency data gathering with reliabilityguaranteeing in heterogeneous wireless sensor networks 【 Author 】 Tian-Yun Shi, Jian Li, Xin-ChunJia, et al. 【 Keywords 】 Heterogeneous wireless sensor networks (HWSNs), datagathering tree, multi-channel, power assignment, linkscheduling 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1074-y 【 Title 】 Recursive Bayesian algorithm foridentification of systems with non-uniformly sampled input data 【 Author 】 Shao-Xue Jing, Tian-HongPan, Zheng-Ming Li 【 Keywords 】 Parameter estimation, discrete time systems, Gaussiannoise, Bayesian algorithm, covariance resetting 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1073-z 点击阅读“人工智能”相关推文 【IJAC热文】MITTomaso Poggio教授探讨深度学习机理 http://mp.weixin.qq.com/s/AwmQyhREjpIew0beIuj6yA 【IJAC推文】周志华、吴建鑫等关于循环神经网络的最新研究成果 http://mp.weixin.qq.com/s/S_7TPZ-QiIHkki2l-KAtDg 【IJAC推文】颜水成团队解读“高智商”机器人的终极杀器——深度学习 http://mp.weixin.qq.com/s/KRBTTycNve3GY8T9AkvoSA
近日，卡内基梅隆大学的 Haohan Wang 和 Bhiksha Raj 在 arXiv 上发布了一篇论文《 On the Origin of Deep Learning 》，详细地梳理了深度学习思想自亚里士多德时代以来的发展，尤其是现代随着计算机科学的兴起而出现的一些新的算法思想，很有参考价值。我们对该文进行了初步的翻译，以方面国内读者阅读。水平有限，翻译不够准确的地方敬请批评指正。有兴趣阅读原文的读者可在 https://arxiv.org/abs/1702.07800 查阅。
还记的IJAC三月发表的一篇有关深度学习的综述吗？作者是麻省理工美国人文与科学院院士Tomaso Poggio 。 Poggio教授在这篇文章中阐述了有关神经网络深度学习的一个基本问题：为什么深层网络比浅层网络更好？ 这篇论文在发表后近2个月，下载量已经超过1500次，Altmetric分数已达到57，该论文和IJAC期刊已被MIT News、ECN等5家国外媒体报道，此后被“机器之心”微信公众号等国内自媒体和网页翻译转载。 这么热的论文，你怎么能不看呢？ Why and When Can Deep-but Not Shallow-networks Avoid theCurse of Dimensionality: A Review Author ： Tomaso PoggioHrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao Institute ： Massachusetts Institute of Technology, USA Keywords: Machine learning, neural networks, deep andshallow networks, convolutional neural networks, function approximation, deeplearning. Full Text: https://link.springer.com/article/10.1007/s11633-017-1054-2 http://news.mit.edu/2017/explained-neural-networks-deep-learning-0414
5 月5日，中国图象图形学学会主办、视觉大数据专业委员会承办、图像视频大数据产业技术创新战略联盟协办的 “CSIG视觉大数据专委会成立大会暨第一届视觉大数据高峰论坛” 在中科院自动化所召开。 小编在会场推介了“Highlight Articles of IJAC in 2016-2017”,推荐的8篇文章中，有三篇是关于深度学习的综述和研究论文。一位老师说IJAC发文级别越来越高喽，呵呵！ 本次论坛以“视觉大数据”为主题，邀请了旷视科技首席科学家孙剑博士、腾讯AI Lab计算机视觉中心总监刘威博士、商汤科技首席研发总监林倞教授、复旦大学姜育刚教授做主题报告。他们分别介绍了自己所从事的视觉大数据领域的研究工作，并就如何加快我国视觉大数据产业的发展，强化视觉大数据技术对智能产业的引领和支持，推动视觉大数据技术不断发展等方面提出了许多独到见解。 （部分文字与照片来源中科院自动化所新闻） Highlight Articlesof IJAC in 2016-2017 Why and When Can Deep-but Not Shallow-networks Avoid theCurse of Dimensionality: A Review Author ： Tomaso PoggioHrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao Institute ： Massachusetts Institute of Technology, USA Keywords: Machine learning, neural networks, deep andshallow networks, convolutional neural networks, function approximation, deeplearning. Full Text: https://link.springer.com/article/10.1007/s11633-017-1054-2 A Survey on Deep Learning-based Fine-grained ObjectClassification and SemanticSegmentation Author ： Bo Zhao, Jiashi Feng, Xiao Wu, Shuicheng Yan Institute ： National University of Singapore, Singapore Keywords: Deep learning, fine-grained imageclassification, semantic segmentation, convolutional neural network, recurrentneural network. Full Text: https://link.springer.com/article/10.1007/s11633-017-1053-3 http://www.ijac.net/EN/abstract/abstract1901.shtml Minimal Gated Unit for Recurrent Neural Networks Author ： Guo-Bing Zhou, Jianxin Wu, Chen-Lin Zhang, Zhi-Hua Zhou Institute ： Nanjing University, China Keywords: Recurrent neural network, minimal gated unit,gated unit, gate recurrent unit, long short-term memory, deep learning. Full Text: https://link.springer.com/article/10.1007/s11633-016-1006-2 http://www.ijac.net/EN/abstract/abstract1822.shtml Pinning Control and Controllability of Complex DynamicalNetworks Author ： Guanrong Chen Institute ： City University of Hong Kong, China Keywords: Complex network, pinning control,controllability, linear time-invariant system, temporally switching network,graph theory. Full Text: https://link.springer.com/article/10.1007/s11633-016-1052-9 http://www.ijac.net/EN/abstract/abstract1865.shtml Review of Some Advances and Applications in Real-timeHigh-speed Vision: Our Views and Experiences Author ： Qing-Yi Gu, Idaku Ishii Institute ： Hiroshima University, Japan Keywords: Real-timehigh-speed vision, target tracking, abnormal behavior detection, behaviormining, vibration analysis, 3D shape measurement, cell sorting. Full Text: https://link.springer.com/article/10.1007/s11633-016-1024-0 http://www.ijac.net/EN/abstract/abstract1808.shtml Cooperative Formation Control of Autonomous UnderwaterVehicles: An Overview Author ： Bikramaditya Das, Bidyadhar Subudhi, Bibhuti Bhusan Pati Institute ： Veer Surendra Sai University of Technology, India Keywords: Autonomous underwater vehicles, cooperativecontrol, formation control, tracking control, regulatory control. Full Text: https://link.springer.com/article/10.1007/s11633-016-1004-4 http://www.ijac.net/EN/abstract/abstract1821.shtml Optimal Feedback Control of OilReservoir Waterflooding Processes Author ： A. S. Grema, Yi Cao Institute ： Cranfield University, UK Keywords: Oil reservoir management,intelligent wells, optimal control theory, feedback control, geologicaluncertainty. Full Text: https://link.springer.com/article/10.1007/s11633-015-0909-7 http://www.ijac.net/EN/abstract/abstract1769.shtml Output Feedback Stabilization of Spacecraft AutonomousRendezvous Subject to Actuator Saturation Author ： Qian Wang, Guang-Ren Duan Institute ： Harbin Institute of Technology, China Keywords: Dynamic gain scheduling, output feedback,parametric Lyapunov equation, input saturation, spacecraft rendezvous. Full Text: https://link.springer.com/article/10.1007/s11633-016-0952-z http://www.ijac.net/EN/abstract/abstract1691.shtml
分类学工作者采集到大量标本，可以研究物种的形态和遗传的变异范围，是一件非常令人兴奋的事情。但有时，大量小型标本的分拣也是令人头疼的事情。智能手机、数码相机和互联网时代，随时随地可能获得有趣的图片，便于专家进行识别。然而，当相同物种的图片增加到一定程度，专家也无法及时给予回复。这里面最大的问题，还在于物种图片鉴别速度和鉴别准确率的问题。我在上一篇博文中提到“开发识别技术，加速物种分类学研究进程”。分子生物学和分类学整合，在过去的几十年极大推动了物种界定和生物系统学的发展。在快速逼近的大数据时代和深度学习时代，物种图片识别技术是否能够得到快速发展？生物分类的过程，实际上是人类智力和时间高度参与人工图片识别和归类的过程。能否加速图片识别，降低人类对重复图片识别和归类的时间投入？ 2012年，中国科学院网络中心的李健博士等就已经实现了鸟类的图片识别。该物种形态学鉴别系统基于 IT 技术，包括了 B/S 架构的 Web 端和手持设备端两部分。使得用户可直接访问专家数据库以判定个体从属的种类，同时也可以上传信息请求专家审核.此鉴别系统在为用户提供方便准确的物种鉴别手段的同时,也为分类学家提供了一个获取大量信息的平台（鸟类物种形态学鉴别系统设计与实现：http://escj.cnic.cn/CN/abstract/abstract12648.shtml）。目前，他的团队已经能够处理动态视频中的鸟类物种识别问题。这样的工作及其平台，能否移植到昆虫工作中？ 今天早晨读到Nvidia网站上一篇关于野生动物图片自动鉴别的报道，同时阅读了昆虫图像识别的一篇综述。两者分别从硬件和软件的角度，提出了一些有价值的思路，值得参考。它们的共同点如下： 1、相机监测网络已经获得大量野生小型兽类、鸟类的图片； 2、像素得到极大提升的智能手机和数码相机已经非常普及，和植物图片一样，昆虫的图片数据也在不断增加； 3、室内分类学工作生成大量的专业级（专家级）图片； 4、野外相机监测、智能手机和数码相机获得图片不一定和室内的专业图片相同的大小、摆放位置和角度； 5、均可以引入深度学习的理念，在未来海量图片的基础上，整合专业级图片，加速图像识别和自动鉴别，并提高准确程度； 6、把分类学者从标本分拣的琐碎工作中解放出来，既能够获得物种的分布数据，又能够集中精力针对部分标本开展深入的物种研究工作。 下列3个图和1个表格均来自： A survey on image-based insect classification.pdf 。 图1、特征提取方式演变 图2、昆虫识别的特征分类策略 图3、分类策略 表1、综述中涉及的昆虫图像识别数据及文献 Automatically Identify Wild Animals in Camera-Trap Images April 11, 2017 A research team led by University of Wyoming developed a deep learning -based system to identify species in the Serengeti National Park in Tanzania that could make it easier for ecologists to track animals in the wild. Camera traps automatically take pictures of passing animals when triggered by heat and motion which produce millions of images and extracting knowledge from these camera-trap images is traditionally done by humans. According to their research paper , it currently takes two to three months for a group of thousands of people to manually label each 6-month batch of images. When lead researcher Jeff Clune at the University of Wyoming in Laramie heard about the project, he knew it was an ideal opportunity to leverage deep learning. Using a variety of GPUs including the NVIDIA DGX-1 AI supercomputer and CUDA , cuDNN and NCLL with the Torch deep learning framework, the researchers trained their deep convolutional neural network to recognize 48 species in 3.2 million images taken from the national park’s hidden camera-traps. They were able to train their neural networks to automatically identify the variety of animals with over 92% accuracy. A sample camera-trap image from the Snapshot Serengeti dataset. “This is very exciting,” says Chris Carbone at the Zoological Society of London. Automatic species recognition could help us learn more about the distribution of species and get a better idea of the impact humans are having on them, he says. Read more Tags: CUDA , cuDNN , GeForce , Higher Education / Academia , Image Recognition , Machine Learning Artificial Intelligence , Tesla
一直以来，棋类游戏都被视为顶级人类智力的试金石。1997年，国际象棋机器人第一次打败人类顶尖高手；9年后，人类最后一次打败国际象棋机器人。 围棋，因其需要计算的变化数量远远超过宇宙中已观测到的原子数量，令一众企图借蛮力穷尽算法的研究者们望而却步。然好景不长，继去年阿法狗大败九段手李世石后，人机大战2.0版也将于今年5月在乌镇正式开打。而支撑“高智商”机器人征战南北的终极杀器，正是火遍全球的“深度学习”技术。 说起“深度学习”，不禁联想到各大互联网公司、智商完美碾压小编的首席科学家们，其中当然少不了AI界大神、360首席科学家、人工智能研究院院长颜水成。 “深度学习”技术的本质就在于特征分层不依赖于研究者的设计，而是机器本身基于一种通用的学习程序，借助数据，像人脑一样主动学习的过程。“深度学习”有大量不同的架构方法，其中就包括基于卷积神经网络的架构方法和基于递归神经网络的架构方法。 脑容量够大、觉得不过瘾的各位不妨继续深入读读颜水成团队关于“深度学习”的研究综述。他们回顾了基于“深度学习”的4种细粒度图像分类方法，以及基于“深度学习”的语义分割方法。如何让机器学会“认识”各种各样的鸟？如何让机器能够“看图说话”？答案就在文中~ 1. 收录信息： Bo Zhao, Jiashi Feng, Xiao Wu,Shuicheng Yan. A Survey on Deep Learning-based Fine-grained ObjectClassification and Semantic Segmentation. InternationalJournal of Automation and Computing , vol. 14, no. 2, pp. 119-135, 2017. 2. 全文链接： 1） Springer Link: https://link.springer.com/article/10.1007/s11633-017-1053-3 2） IJAC 官网： http://www.ijac.net/EN/abstract/abstract1901.shtml 3. 摘要： The deep learning technology hasshown impressive performance in various vision tasks such as image classification,object detection and semantic segmentation. In particular, recent advances ofdeep learning techniques bring encouraging performance to fine-grained imageclassification which aims to distinguish subordinate-level categories, such asbird species or dog breeds. This task is extremely challenging due to highintra-class and low inter-class variance. In this paper, we review four typesof deep learning based fine-grained image classification approaches, includingthe general convolutional neural networks (CNNs), part detection based,ensemble of networks based and visual attention based fine-grained imageclassification approaches. Besides, the deep learning based semanticsegmentation approaches are also covered in this paper. The region proposal basedand fully convolutional networks based approaches for semantic segmentation areintroduced respectively. 4. 关键词： Deep learning ， fine-grained image classification ， semanticsegmentation ， convolutional neural network (CNN) ， recurrent neural network (RNN). IJAC 近期发表的其他两篇深度学习热文如下 ： TomasoPoggio, Hrushikesh Mhaskar, Lorenzo Rosasco, Brando Miranda, Qianli Liao. Why and When Can Deep-but NotShallow-networks Avoid the Curse of Dimensionality: A Review . International Journal of Automation andComputing , DOI: 10.1007/s11633-017-1054-2, 2017. 全文链接 ： https://link.springer.com/article/10.1007/s11633-017-1054-2 Guo-BingZhou, Jianxin Wu,Chen-Lin, ZhangZhi-Hua Zhou. Minimal gated unit for recurrent neural networks . I nternational Journal of Automation andComputing , Vol.13, No.3, pp. 226-234, 2016. 全文链接 ： https://link.springer.com/article/10.1007/s11633-016-1006-2 或 http://www.ijac.net/EN/abstract/abstract1822.shtml 5. 作者简介： 1 ） BoZhao received the B. Sc. degree in networking engineeringfrom Southwest Jiaotong University in 2010. He is a Ph.D. degree candidate atSchool of Information Science and Technology, Southwest Jiaotong University,China. Currently, he is at the Department of Electrical and ComputerEngineering, National University of Singapore, Singapore as a visiting scholar. His research interests includemultimedia, computer vision and machine learning. E-mail: zhaobo@my.swjtu.edu.cn ORCID iD: 0000-0002-2120-2571 2 ） JiashiFeng received the B.Eng. degree fromUniversity of Science and Technology, China in 2007, and the Ph.D. degree fromNational University of Singapore, Singapore in 2014. He was a postdocresearcher at University of California, USA from 2014 to 2015. He is currentlyan assistant professor at Department of Electrical and Computer Engineering,National University of Singapore, Singapore. His research interests includemachine learning and computer vision techniques for large-scale data analysis.Specifically, he has done work in object recognition, deep learning, machinelearning, high-dimensional statistics and big data analysis. E-mail: elefjia@nus.edu.sg 3 ） XiaoWu received the B.Eng. and M. Sc. degrees in computerscience from Yunnan University, China in 1999 and 2002, respectively, and thePh.D. degree in computer science from City University of Hong Kong, China in2008. He is an associate professor at Southwest Jiaotong University, China. Heis the assistant dean of School of Information Science and Technology, and thehead of Department of Computer Science and Technology. Currently, he is atSchool of Information and Computer Science, University of California, USA as avisiting associate professor. He was a research assistant and a senior researchassociate at the City University of Hong Kong, China from 2003 to 2004, and2007 to 2009, respectively. From 2006 to 2007, he was with the School ofComputer Science, Carnegie Mellon University, USA as a visiting scholar. He waswith the Institute of Software, Chinese Academy of Sciences, China, from 2001to 2002. He received the second prize of Natural Science Award of the Ministryof Education, China in 2015. His research interests includemultimedia information retrieval, image/video computing and data mining. E-mail: wuxiaohk@gmail.com (Corresponding author) ORCID iD: 0000-0002-8322-8558 4 ） ShuichengYan is currently an associate professor at theDepartment of Electrical and Computer Engineering, National University ofSingapore, Singapore, the founding lead of the Learning and Vision ResearchGroup (http://www.lvnus.org). He has authored/co-authored nearly 400 technicalpapers over a wide range of research topics, with Google Scholar citation12000 times. He is ISI highly-cited researcher 2014, and IAPR Fellow 2014. He hasbeen serving as an associate editor of IEEE Transactions on Knowledge and DataEngineering, Computer Vision and Image Understanding and IEEE Transactions onCircuits and Systems for Video Technology. He received the Best Paper Awardsfrom ACM MM’13 (Best paper and Best student paper), ACM MM’12 (Best demo),PCM’11, ACM MM’10, ICME’10 and ICIMCS’09, the runnerup prize of ILSVRC’13, thewinner prizes of the classification task in PASCAL VOC 2010–2012, the winnerprize of the segmentation task in PASCAL VOC 2012, the honorable mention prizeof the detection task in PASCAL VOC’10, 2010 TCSVT Best Associate Editor (BAE)Award, 2010 Young Faculty Research Award, 2011 Singapore Young Scientist Award,and 2012 NUS Young Researcher Award. His research interests includemachine learning, computer vision and multimedia. E-mail:eleyans@nus.edu.sg
上周，在IJAC优先在线发表的几篇论文中，麻省理工美国人文与科学院院士Tomaso Poggio的一篇有关深度学习的综述成为一大亮点。Poggio教授在这篇文章中阐述了有关神经网络深度学习的一个基本问题：为什么深层网络比浅层网络更好？ 文章内容延续了Poggio教授在2016年8月在中国人工智能大会（2016CCAI）上的演讲报告《The Science and the Engineering ofIntelligence》。 图1 来源于2016 CCAI 演讲PPT 图2 来源于2016 CCAI 演讲PPT “ 深度学习架构和机器学习模式的搭建，来自于神经学方面的研究进展，换句话说，同样的架构是存在于大脑皮质当中的。 关于深度学习，已经有成千上万的研究者在不同领域进行这方面的研究，比如无人驾驶、语音识别等等。可是我们还不清楚，为什么深度学习在这些工程应用中会起作用，深度学习的机理是什么？ 我们对这个问题很感兴趣另外一个原因是：探讨深度学习的机理也将有助于我们理解‘为什么大脑皮质会存在一些不同的层次？’ ” Poggio 教授在这篇文章中，将为您解读深度学习的关键理论、最新成果和开放式研究问题。 同时这篇文章也是IJAC即将发表的 Special Issue on Human Inspired Computing 中的一篇文章。该专题其他热文将陆续优先在线发表，敬请期待。 一点点题外话：小编在去年的CCAI大会上有幸拜访了Poggio教授，教授博学、谦逊而富有亲和力的形象给小编也留下深刻印象。他曾提到：期望能帮助年轻人更好的了解神经科学、理解机器学习。如果要在智能方面走得远，不能只靠计算机，还需要与人类本身的研究相互结合，才能碰撞出更多的东西。 接下来，小编将为您奉上这篇文章的具体信息，以及IJAC近期在线发表的其他文章链接，欢迎下载阅读 【 Title 】 Why and when can deep-but not shallow-networksavoid the curse of dimensionality: A review 【 Author 】 Tomaso Poggio, Hrushikesh Mhaskar, LorenzoRosasco, Brando Miranda, Qianli Liao 【 Abstract 】 The paper reviews and extends an emerging bodyof theoretical results on deep learning including the conditions under which itcan be exponentially better than shallow learning. A class of deepconvolutional networks represent an important special case of these conditions,though weight sharing is not the main reason for their exponential advantage.Implications of a few key theorems are discussed, together with new results,open problems and conjectures. 【 Keywords 】 Machine learning, neural networks, deep andshallow networks, convolutional neural networks, function approximation, deeplearning 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1054-2 【 Publish date 】 Published online March 14, 2017 The other recentlypublished online papers include: 【 Title 】 Improvement of wired drill pipe data qualityvia data validation and reconciliatio 【 Author 】 Dan Sui, Olha Sukhoboka, Bernt Sigve Aadnøy 【 Keywords 】 Data quality, wired drill pipe (WDP), datavalidation and reconciliation (DVR), drilling models 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1068-9 【 Publish date 】 Published online March 4, 2017 【 Title 】 Reaction torque control of redundantfree-floating space robot 【 Author 】 Ming-He Jin, Cheng Zhou, Ye-Chao Liu, Zi-QiLiu, Hong Liu 【 Keywords 】 Redundant space robot, reaction torque,reactionless control, base disturbance minimization, Linux/real timeapplication interface (RTAI) 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1067-x 【 Publish date 】 Published online March 4, 2017 【 Title 】 A piecewise switched linear approach fortraffic flow modeling 【 Author 】 Abdelhafid Zeroual,Nadhir Messai, SihemKechida, Fatiha Hamdi 【 Keywords 】 Switched systems, modeling, macroscopic,traffic flow, data calibration 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1060-4 【 Publish date 】 Published online March 4, 2017 【 Title 】 Navigation of non-holonomic mobile robot usingneuro-fuzzy logic with integrated safe boundary algorithm 【 Author 】 A. Mallikarjuna Rao, K. Ramji, B. S. K.Sundara Siva Rao, V. Vasu, C. Puneeth 【 Keywords 】 Robotics, autonomous mobile robot (AMR),navigation, fuzzy logic, neural networks, adaptive neuro-fuzzy inference system(ANFIS), safe boundary algorithm 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-016-1042-y 【 Publish date 】 Published online March 4, 2017 【 Title 】 Method for visual localization of oil and gaswellhead based on distance function of projected features 【 Author 】 Ying Xie, Xiang-Dong Yang, Zhi Liu, Shu-NanRen, Ken Chen 【 Keywords 】 Robot vision, visual localization, 3D objectlocalization, model based pose estimation, distance function of projectedfeatures, nonlinear least squares, random sample consensus (RANSAC) 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-017-1063-1 【 Publish date 】 Published online March 4, 2017 【 Title 】 Virtual plate based controlling strategy oftoy play for robots communication development in JA space 【 Author 】 Wei Wang, Xiao-Dan Huang 【 Keywords 】 Human robot cooperation, joint attention (JA)space, reachable space, toy play ability, a virtual plate 【 Full Text 】 https://link.springer.com/article/10.1007/s11633-016-1022-2 【 Publish date 】 Published online February 21, 2017 阅读更多IJAC优先在线出版论文： https://link.springer.com/journal/11633
标签: 深度学习
