New funding pushes chinese ai chipmaker horizon robotics. Ludwig is a toolbox built on top of tensorflow that allows to train and test deep learning. Oct 25, 2019 the application of deep learning algorithms has the potential to reduce unnecessary referrals and costs in scoliosis screening. Introduction of deep learning what people already knew in 1980s. Sequence discriminative training for deep learning based acoustic keyword spotting zhehuai chen, student member, ieee, yanmin qian, member, ieee, and kai yu, senior member, ieee abstractspeech recognition is a sequence prediction problem. Deep convolutional priors for indoor scene synthesis. Deep networks achieved best results on many tasksdatasets 2. Previously in this blog, we have mentioned that baidu a dominant search engine in china is opening institute of deep learning.
Sequence discriminative training for deep learning based acoustic keyword spotting. Deng and yu 2014 described deep learning classes and techniques, and applications of. The task is highly challenging, largely due to the lack of a meaningful regular. Three types of networks are used to extract deep features. Exact solutions to the nonlinear dynamics of learning in deep linear neural networks. Reshaping deep neural network for fast decoding by nodepruning. Formerly head of baidus institute of deep learning, kai.
Ieee conference on computer vision and pattern recognition cvpr, 2009. Xin lu, zhe lin, hailin jin, jianchao yang, james z. Pdf tandem deep features for textdependent speaker. Rob fergus rob fergus is an assistant professor of computer science at the courant institute of mathematical sciences, new york university. In this course, you will learn the foundations of deep learning, understand how to build neural networks, and learn how to lead successful machine learning projects. Cs 229 machine learning final projects, autumn 2014.
Largescale deep learning at baidu proceedings of the 22nd. Deep convolutional priors for indoor scene synthesis kai wang,brown university manolis savva,princeton university angel x. An introduction to nonparametric hierarchical bayesian modelling with a focus on multiagent learning pdf volker tresp and kai yu. Feature learning for image classification by kai yu and andrew ng.
Stacked hourglass networks for human pose estimation. Deep learning computer vision speech recognition language understanding robotics. Sequence discriminative training for deep learning based. Deep learning, yoshua bengio, ian goodfellow, aaron courville, mit press, in preparation.
Domain specific sentiment analysis using crossdomain data. Chapter 9 is devoted to selected applications of deep learning to information retrieval including web search. Make learning algorithms much better and easier to use. In addition, building on the deep learning framework, i will present two new algorithms, sparse deep belief networks and convolutional deep belief networks, for building.
Kai yu previously, he was head of the media analytics department of nec labs in silicon valley, california, leading the development of intelligent systems for machine learning, image recognition, multimedia search, video surveillance, recommendation, data. He received a masters in electrical engineering with prof. Deep learning of invariant features via simulated fixations. Horizon robotics was founded in 2015 by kai yu, who had previously founded and led the baidu institute of deep learning. There are many resources out there, i have tried to not make a long list of them. These 20 leading technologists are driving chinas ai revolution. Andrew zisserman at the university of oxford in 2005. A couple years ago, baidu hired kai yu, a engineer skilled in artificial intelligence. Deep learning andrew ng thanks to adam coates, kai yu, tong zhang, sameep tandon, swati dube, brody huval, tao wang. Kai chen is a vice director at sensetime, leading the algorithm platform team of eig research eig. The human brain is composed of multiple modular subsystems, with a unique way interacting among each other.
Applying deep learning to derive insights about noncoding regions of the genome. Besides employing various deep learning approaches for frame. In proceedings of the 30th international conference on machine learning icml pp. Do chinese publicly listed companies adjust their capital structure toward a target level. Visualized insights into the optimization landscape of fully convolutional networks author. If this repository helps you in anyway, show your love. Pdf robust deep feature for spoofing detection the sjtu. Applying deep learning to derive insights about noncoding regions of the. Deep learning with kernel regularization for visual recognition. We believe a comprehensive coverage of the latest advances on image feature learning will be of broad interest to eccv attendees.
Discover the fundamental computational principles that underlie perception, make progress on ai. Sparse coding and deep learning is best method currently for many tasks. View kai yus profile on linkedin, the worlds largest professional community. Yanmin qian 1, kai yu 1, shinji watanabe 2 1 speechlab, department of computer science and engineering, shanghai jiao tong university. Deep learning based prediction of speciesspecific protein. Machine learning and ai via brain simulations andrew ng. This paper describes an investigation of using various types of deep features in a tandem fashion for textdependent speaker verification. Prior to this, i was a visiting research scientist at facebook ai research and a research scientist at eloquent labs working on dialogue. While at baidu, yu launched a number of highprofile ai projects, including. We study the determinants of capital structure for 650 chinese publicly listed companies over the period from 1999 to 2004. Make revolutionary advances in machine learning and ai. Deep learning, feature learning image classification using sparse coding, pt. If you are just starting out in the field of deep learning or you had some experience with neural networks some time ago, you may be confused. Proceedings of the 27th international conference on machine learning.
You will learn about convolutional networks, rnns, lstm, adam, dropout, batchnorm, and more. Currently, deep learning systems with multiple servers and multiple gpus are usually implemented in a single cluster, which typically employs infiniband fabric to support remote direct memory access rdma, so as to achieve high throughput and. Deep multiple instance learning for image classification. I am an assistant professor of computer science at brown university. Structured deep learning for context awareness in speech and language processing kai yu shanghai jiaotong university. See the complete profile on linkedin and discover kais connections and. Domain adaptive ensemble learning kaiyang zhou, yongxin yang, yu qiao, tao xiang tech report, 2020. Based on inferences from these approaches, we discuss how deep learning methods can benefit the field of biometrics and the potential gaps that deep learning approaches need to address for realworld biometric applications. I build intelligent machines that understand the visual world and can help people be visually creative.
In recent two years, deep learning has made many performance breakthroughs, for example, in the areas of image understanding and speech recognition. First, objects may appear with different sizes in a single image, e. My research interests are in computer vision and deep learning. Pdf project jianchao yang, kai yu, yihong gong, and thomas huang. That is, using probabilistic frameworks to formulate learning problems and to inferestimate model parameters. Deep domainadversarial image generation for domain generalisation kaiyang zhou, yongxin yang, timothy hospedales, tao xiang aaai, 2020. Pdf binary deep neural networks for speech recognition. Deep learning of invariant features via simulated fixations in video. Forbes takes privacy seriously and is committed to transparency. Deep learning with kernel regularization for visual. Development and validation of deep learning algorithms for. Deep learning of invariant features via simulated fixations in video will y. Although deep neural networks dnn has achieved signifi. Second, the essential contextual information of an object may occupy a much larger area than the.
How many training data points for deep learning to work. Introduction in the deep learning era, singlespeaker. Sglutathionization is one of the posttranslational modifications ptm modification of sglutathionylation is different in homo sapiens and mus musculus prediction model of speciesspecific sglutathionylation sites is based on deep learning algorithm to facilitate the users basic research, a novel online service deepgsh is constructed. Ng neural information processing systems nips workshop on deep learning and unsupervised feature learning, 2011 haptic belt with pedestrian detection tech demo. Mit deep learning book beautiful and flawless pdf version mit deep learning book in pdf format complete and parts by ian goodfellow, yoshua bengio and aaron courville. Improved local coordinate coding using local tangents pdf. In chapter 10, we cover selected applications of deep learning to image object recognition in computer vision. Tricks of the trade, reloaded, springer lncs, 2012. Kai yu s pascal voc object recognition result 2009 feature best of. Institute for pure and applied mathematics, ucla july 18, 2012 for. Element of neural network z a 1 w 1 a 2 w 2 a k w k b.
Jurgen schmidhuber, deep learning and neural networks. As of 2015, a rough rule of thumb is that a supervised deep learning algorithm will generally achieve acceptable performance with around 5,000 labeled examples per category, and will match or exceed human performance when trained with a dataset containing at least 10 million labeled examples. Since 2006, learning highlevel features using deep architectures from raw data has become a huge wave of new learning paradigms. My team innovates search technologies and products everyday, by making better use of speech, images, videos, and musics. Largescale deep learning at baidu proceedings of the.
Machine learning authorstitles recent submissions 75. Weaklysupervised learning of midlevel features for pedestrian attribute recognition and localization stateoftheart methods treat pedestrian attribute recognition as a mul. Structured deep learning for context awareness in speech and. In my research, im broadly interested in the intersection of computer graphics with artificial intelligence and machine learning. Yukai is at the cutting edge of the field of behavioral design. In this talk, i will walk through some of the latest technology advances of deep learning within baidu, and discuss the main challenges, e. I think kai ships deep learning to an incredible number of. Their combined citations are counted only for the first article. Communication efficient distributed machine learning with the parameter server mu li, dave andersen, alex smola, and kai yu in neural information processing systems, 2014.
Kai yu previously, he was head of the media analytics department of nec labs in silicon valley, california, leading the development of intelligent systems for machine learning, image recognition, multimedia search, video surveillance. On the importance of initialization and momentum in deep learning. We find that most deep learning research in biometrics has been focused on face and speaker recognition. Wen zhang, kai shu, suhang wang, huan liu, and yalin wang. I am a deputy engeering director of baidu, managing the companys multimedia department. Deep learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. Mu li, amr ahmed and alex smola in acm international conference on web search and data mining, 2015 paper, slides. Ng2, kai yu3 1department of electrical engineering, stanford university, ca 2department of computer science, stanford university, ca 3nec laboratories america, inc. Deep learning with kernel regularization for visual recognition kai yu, wei xu. Abstract in this paper we aim to train deep neural networks for rapid visual recognition. Progresses and perspectives kai yi, shitao chen, yu chen, chao xia, nanning zheng, 14th international conference on artificial intelligence applications and innovations aiai 2018, rhodes, greece. Communication efficient distributed machine learning with the parameter server mu li, dave andersen, alex smola, and kai yu.
Aaai 2019 workshop on network interpretability for deep. Shulin zeng, guohao dai, hanbo sun, kai zhong, guangjun ge, kaiyuan guo, yu wang, huazhong yang, enabling efficient and flexible fpga virtualization for deep learning in the cloud, to appear in international symposium on fieldprogrammable custom computing machines fccm, 2020. Fast and flexible indoor scene synthesis via deep convolutional generative models daniel ritchie. F 1 introduction v isual patterns occur at multiscales in natural scenes as shown in fig. Deep neural networks dnns are widely used in most cur. Although deep learning has been successfully used in acoustic modeling of speech recognition, it has not been thoroughly investigated and widely accepted for speaker verification.
In deep learning, the function is represented by neural network. Ng2, kai yu3 1department of electrical engineering, stanford university, ca. The 2018 ieee international conference on data mining icdm 2018 regular paper multimodal fusion of brain networks with longitudinal couplings. Mlslp 16 8 the typical speech inputs, with static, delta and double delta features, can. Kai shu, suhang wang, thai le, dongwon lee, and huan liu. Clearly, there is a progressive increment of publications that could describe an. Chang,princeton university daniel ritchie,brown university dataset synthesized om living o. Thanks to adam coates, kai yu, tong zhang, sameep tandon. If you also have a dl reading list, please share it with me. Angel xuan chang i am an assistant professor at simon fraser university. In chapters 8, we present recent results of applying deep learning to language modeling and natural language processing. Sep 27, 2019 mit deep learning book beautiful and flawless pdf version mit deep learning book in pdf format complete and parts by ian goodfellow, yoshua bengio and aaron courville. I believe this is our best shot at progress towards. According to a recent news in wired, baidu has opened its research facility on deep learning in silicon valley at san francisco cupertino.
Unsupervised learning of visual invariance with temporal coherence. Structured deep learning for context awareness in speech. Neural networks, machine learning, deep learning, recent advances. A deeplearning architecture is a mul tilayer stack of simple mod ules, all or most of which are subject to learning, and man y of which compute nonlinea r inputoutpu t mappings. Unsupervised feature learning and deep learning andrew ng thanks to. Kai yu hollywood2 classification accuracy prior art laptev et al. My research include but are not limited to probabilistic graphical models, bayesian nonparametric, approximate inference, bayesian deep learning, sparse learning, largescale machine learning and kernel methods. The deep learning tutorials are a walkthrough with code for several important deep architectures in progress. Aaai 2019 workshop on network interpretability for deep learning link. Ieee transactions on pattern analysis and machine intelligence 35 1.