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Dear Valued Guest:
We cordially invite you to attend the Intel® AI DevCon (Intel® AIDC) from November 14-15, 2018 at the China World Summit Wing in Beijing. This world-class AI conference will bring together over 2,000 of the world's most influential AI experts, industry leaders, data architects, and technical engineers. Over 30 industry partners will showcase cutting-edge solutions, products, and technological applications in the AI sector. This conference will focus on technological frontiers, trends, opportunities and challenges related to AI. This will be a wonderful opportunity for you to meet face-to-face with experts in the field and help create the world's top platform for AI partnerships.
At the Intel® AIDC 2018 we will explore cutting edge topics in AI with leading experts in the field, while offering a unique conference experience. We hereby invite you to attend this summit, and hope you will join us in our journey to drive the transformation of industry through AI technology.
Cordially Invited by Intel China
Intel® Myriad™ X third generation VPU delivers world class-leading performance in computer vision and deep neural network inferencing.
AI accelerator demo: one or multi-ways video streaming input via USB is inferenced with typical neural network algorithms. Human bodies are detected and identified, the frame rate is about 60 fps.
High performance Smart Camera demo: Camera input to Myriad™ X’s own ISP; through image pre-processing, objects detected by mobileNetSS, object recognized by mobileNet and output NN analysis result to display on PC via USB, the frame rate is about 30 fps.
Intel ® Movidius ™ Myriad ™ X based Vision Accelerator is a product that provides powerful deep neural network inference capabilities for fast and accurate video processing together with Intel OpenVINO ™ toolkit, to meet the needs of edge computer vision applications and help solution providers and their customers take full advantage of video analysis in more practical scenarios .
Ferrari Challenge is a single model race series. Intel® is applying AI to the drone-based race coverage. Solution uses fine grain classification and few shot learning to match each car to its driver.
Coach is an open source Python* RL framework for AI agent development and training. It supports many state-of-the-art RL algorithms, integrated with multiple simulation environments.
OpenVINO™ is an Intel® FREE toolkit to fast-track high-performance computer vision and deep learning inference to vision applications. In this demo we show OpenVINO™ deep learning application running on Intel® FPGA PAC board (Arria® 10), which uses deep learning for Vehicle and Chinese License Plates Detector, Vehicle Color/Type Recognition and Chinese License Plates Recognition.
Introduce the why, what and how of the Unified Big Data Analytics and AI Platform - Analytics Zoo, which is based on Apache Spark and BigDL™. This demo shows example source code of Analytics Zoo to achieve practical demo such as “Soccer Star Recognition”, “Image generation”, etc.
DL Boost is a set of processor technologies designed to accelerate AI deep learning use cases. It extends Intel® AVX-512 with a new Vector Neural Network Instruction.11X speed up in performance delivers a better TCO for customers and a more consistent experience for end users.
Introducing a New Generation of Versatile, Productive Plug-and-Play AI development kit, powered by the Intel® Movidius™ Myriad™ X VPU.Delivering up to 8X the performance of its predecessor.
PERFORMANCE IMPROVEMENT OF TRAINING TIME OF M-CNN USING HIGH-CONTENT IMAGING.
Supported by Inspur's NF5280M5 AI Server with 4 F10A FPGA cards, this live demo shows FPGA-based acceleration for real-time image classification is enabling high efficiency, low latency, and high performance image recognition with high accuracy. Inspur F10A is equipped with Intel® Arria™ 10 FPGA.
Corerain AI Edge Solution uses Intel Arria™ 10 FPGA 660 SoC and Corerain self-developed chip structure, CAISA, providing powerful AI real time detection solution, which can support 16 full HD surveillance cameras to detect faces, cars, and traffic situation with the power consumption of 36W.
Horizon Robotics 360° visual perception system consists of three sets of Matrix that are in-house developed by ourselves. By designing the position of mounted cameras, our computing platform for autonomous driving can achieve a 360-degree perception around the vehicle.
XForce is our newly launched FPGA Edge AI computing platform based on Horizon's BPU 2.0 AI Processor architecture. With the hardware deeply customized to seamlessly run our proprietary algorithms, XForce provides superior visual perception capabilities with extraordinary efficiency in cost and power consumption compared with generic GPU solutions. XForce now supports functionalities of human behavior analysis and video structuring.
Run deep learning jobs like face recognition, object detection and word vector training using Intel Xeon ®Scalable Processor and Intel Optimized Caffe framework on QingCloud deep learning platform.
SIGHTA is a real-time traffic AI analysis product based on video algorithms and computer vision techs. The core features includes traffic data collection, traffic accidents detection, device status evaluation and highway fog detection.
Through UAI platform, we can help customers to achieve AI training tasks and inference services.
JD Magic Cam, a smart camera developed by China's largest retailer, JD.com that allows people to superimpose their images on different backdrops in real-time, provides a unique marketing solution for offline stores. A real-time mapping function enables users to virtually switch instantly from one scene to the next. Users can add 2D and 3D special effects to each scene, and can seamlessly share images created with JD Magic Cam across social media with friends to aid in purchasing decisions. In addition to providing a source of entertainment for consumers, JD Magic Cam also empowers businesses with enhanced marketing and user engagement capabilities. JD Magic Cam is powered by Intel® i5-6500 processor. expand and upgrade future innovations.
The whole solution is based on huge volume of image data captured on the factory production line in real time which are later pre-processed & sent cloud (Private or public). In the cloud, the AI engines are trained with the data on Xeon Scalable processors. Finally, the inference engine can be implemented on either the cloud side or the front-end Core® i7, depending on the network condition and the latency requirement, to conduct various kinds of vision inspections.
Skysys is the very first team ever in China that is engaged in the development of drones' hangar, aka the Drone Autonomous Operating Platform or the Drone-enabling System. The advent of this particular system, which is named as UltraHive, has offered a one-stop solution that resolves all the drawbacks of current drones, including difficult control, short endurance, lack of hangar and poor universalization.
Sketch Guess This demo presents a Google mini-game 'Quick, Draw!' clone made using HS and Python There are 350 stages in total. In each stage you will be asked to draw a specific object on the paper The neural network in HS will decide if your drawing matches the object's title.
Face Recognition This demo presents a realtime multi-face detection and recognition system that is built with HS, It runs on most platforms with nearly no extra computational cost on the host The system can also register new faces on the fly
Tarsier Module by VIONVISION, based on the unparalleled small-sized Movidius™ chip with low power consumption (about 1.5W) but powerful computing power. Smart Cameras based on Tarsier Module:Smart Face People Counting Camera, All-in-one People Counting Camera,Smart Fisheye Camera.
Based on the Intel® Xeon® SP Platform, Dr. Pecker greatly improves the efficiency and accuracy of medical imaging diagnosis, and reduces the rate of missed diagnosis and misdiagnosis.
Intel and HuiyiHuiying jointly developed the‘Artificial Intelligence Breast Cancer Full Cycle Health Management Platform’, which covers the entire process from breast cancer prevention screening to follow-up monitoring, providing a reliable end-to-end artificial intelligence solution to hospitals.
Currently,with introduction of 4G and coming 5G,huge number of image and videos contents are generated continuously in the internet, one major issue of the content-provider is to realize an effective detection to avoid unpermitted contents publicated , We demonstrate an effective AI based image detection using Intel® AIO PAC card for certain content. Combining with Vismarty's own AI algorithm and FPGA implementation, an outperformed processing speed will be demonstrated comparing with other solutions.
4Paradigm Prophet AutoML is a low-threshold, automated AI application building tool.4Paradigm AIO's software and hardware integrated design is a genuine combination of the advantages of 4Paradigm's state-of-the-art AutoML technology and Intel® Xeon® SP's advanced hardware architecture.
The edge-to-cloud Industrial Edge Computing platform, train the AI model in cloud and run influence at edge.
CTC-Intel® Joint Lab demonstrate AI related achievement included AIaaS platform and industry-vertical solutions like 'Intelligent police, 'Intelligent farm' and etc.
Intel® provides end-to-end AI solution for wild tiger protection Intelligent camera powered by Intel Movidius is used for onsite recognition of wild animals On cloud, tiger re-identification based on Intel® Xeon® is used for individual tiger’s data extraction and tracing.
Intel® Xeon® help reconstruct high resolution 3D model for Jiankou Great Wall based on the air survey by Intel Falcon 8+ drone Cracks and collapses on the Wall are automatically detected 3D-GAN network generate the missing parts of the Wall This is the first time that AI has been applied in large-scale ancient construction (digital) repair.
Intel® provides end-to-end AI solution for wild tiger protection Intelligent camera powered by Intel Movidius is used for onsite recognition of wild animals On cloud, tiger re-identification based on Intel® Xeon® is used for individual tiger's data extraction and tracing.
Xu (Ian) Yang is corporate vice president and president of Intel China, overseeing all Intel China operations and strategy. Ian Yang is also an MCM member of Intel's global core management team and participates in Intel's top global decisions.
Returning to China in 1995, Yang was named OEM sales manager, responsible for developing PC OEM business in China. In this position, he developed close cooperation between Intel and China's fledgling PC industry for win-win growth. In 2000, Yang was promoted to general manager of Intel China Sales and Marketing Group. Subsequently in 2005 he was appointed Sales and Marketing Group vice president and general manager of Intel's Asia Pacific geography. This was a role he shared with John Antone until Intel China became its own standalone geography in 2007. At that point he assumed the role of Sales and Marketing Group vice president and general manager of Intel China. In 2009 Yang was promoted as president of Intel China.
During Yang's over 30-year Intel career he has also held positions in technical marketing, customer marketing and business development in both the U.S. and China.
In 1990, Ian Yang graduated from GMI Engineering and Management Institute in Flint, Michigan with a bachelor's degree in electrical engineering.
Naveen G. Rao is corporate vice president and general manager of the Artificial Intelligence Products Group at Intel Corporation.
Rao’s team focuses on deep learning, a subset of machine learning and artificial intelligence, and works to develop the hardware and software ingredients needed to enable its scalable deployment. Intel uses deep learning to accelerate complex, data-intensive processes, such as image recognition and natural language processing, to improve the performance of Intel® Xeon® and Intel® Xeon Phi™ processors in various business segments, including autonomous driving and personalized medicine.
Trained as both a computer architect and neuroscientist, Rao joined Intel in 2016 with the acquisition of Nervana Systems. As chief executive officer and co-founder of Nervana, he led the company to become a recognized leader in the deep learning field. Before founding Nervana in 2014, Rao was a neuromorphic machines researcher at Qualcomm Inc., where he focused on neural computation and learning in artificial systems. Rao’s earlier career included engineering roles at Kealia Inc., CALY Networks and Sun Microsystems Inc.
Rao earned a bachelor’s degree in electrical engineering and computer science from Duke University, then spent a decade as a computer architect before going on to earn a Ph.D. in computational neuroscience from Brown University. He has published multiple papers in the area of neural computation in biological systems. Rao has also been granted patents in video compression techniques, with additional patents pending in deep learning hardware and low-precision techniques and in neuromorphic computation.
Zhi-Hua Zhou is a Professor of Nanjing University, China. He is the Head of the Department of Computer Science and Technology, Dean of the School of Artificial Intelligence, and Founding Director of the LAMDA Group. His main research interests are in artificial intelligence, machine learning and data mining. He has；authored the books "Ensemble Methods: Foundations and Algorithms (2012)" and "Machine Learning (in Chinese, 2016)", and published more than 150 papers in top-tier international journals/conferences. According to Google Scholar, his publications have received more than 35,000 citations, with an H-index of 90. He also holds 23 patents and has rich experiences in industrial applications. He has received various awards, including the National Natural Science Award of China, PAKDD Distinguished Contribution Award, IEEE ICDM Outstanding Service Award, etc. He serves as the Executive Editor-in-Chief of Frontiers of Computer Science, and Action/Associate Editor of Machine Learning, IEEE PAMI, ACM TKDD, etc. He founded ACML (Asian Conference on Machine Learning) and served as General Chair of IEEE ICDM 2016, Program Chair of IJCAI 2015 Machine Learning track, etc. He will serve as Program Chair of AAAI 2019 and IJCAI 2021. He is the Chair of CCF-AI, and was Chair of the IEEE CIS Data Mining Technical Committee. He is a foreign member of the Academy of Europe, and a Fellow of the ACM, AAAI, AAAS, IEEE and IAPR.
Long currently takes in charge of the R&D of AI and big data products and services of Tencent Cloud. After receiving bachelor degree from Tsinghua University, he worked in China, Germany and the US for more than 16 years, serving mainly MNCs such as Ebay, Siemens, VMware and Cheetah Mobile etc. Prior to his current role in Tencent, he was responsible for VMware's flagship cloud management product and content recommendation product in Cheetah Mobile.
Jonathan Ballon is Vice President in the Internet of Things Group (IOTG) at Intel Corporation. He is responsible for a global team chartered with driving and accelerating innovation and growth in various market segments. His team is a pioneer in artificial intelligence and deep learning applications with cumputer vision capabilities, supported by software tools & a robust ecosystem. Additionally, he leads the IoT channel routes-to-market and is responsible for Intel's China engineering teams and business. Jonathan is passionate about the application of technology in, our environment, human productivity, safety, education, health. and longevity,
Prior to joining Intel, Ballon served as Chief Strategy Officer and Chief Operating Officer of General Electric's Industrial Internet Business, and served as Corporate Vice President at Cisco leading the office of strategy and planning. He presently also serves as an advisor and board member to several Silicon Valley based startups and accelerators.
Ballon holds a bachelor's degree in economics from the University of California, San Diego, summa cum laude, and an MBA degree in strategic planning and operations management from the University of Pittsburgh.
Jason (Jinquan) Dai is a senior principal engineer and CTO of Big Data Technologies at Intel, where he is responsible for leading the global engineering teams (located in both Silicon Valley and Shanghai) on the development of advanced big data analytics (including distributed machine and deep learning), as well as collaborations with leading research labs (e.g., UC Berkeley AMPLab and RISELab). Jason is an internationally recognized expert on big data, cloud and distributed machine learning; he is the program co-chair of O'Reilly AI Conference in Beijing, a founding committer and PMC member of Apache Spark, a mentor for Apache MXNet, and the creator of the BigDL project.
Casimir Wierzynski is Senior Director, Office of the CTO, in the Artificial Intelligence Product Group at Intel. He leads research efforts to identify, synthesize, and incubate emerging technologies that will enable the next generation of AI systems. Before joining Intel in 2017, Cas led research teams in neuromorphic computing, learning and AI planning, and autonomous robotics at Qualcomm. Prior to Qualcomm, Cas was a Vice President at Goldman Sachs, where he traded fixed income and credit derivatives.
Cas received his BS and MS in electrical engineering at MIT, completing his master’s thesis at AT&T Bell Labs, and a BA in mathematics at Cambridge University as a British Marshall Scholar. Driven by his passion for AI and the brain, Cas left finance to receive his PhD at Caltech in Computation and Neural Systems, where he used large-scale neural recordings to study the relationship between memory consolidation and sleep.
Yinyin Liu has research experience in deep learning, reinforcement learning, neuromorphic computing, and robotics. She and the Intel Nervana team developed open-source deep learning frameworks—neon, and Intel nGraph— and brought state-of-the-art models on image recognition, image localization, and natural language processing into the framework and deep learning solutions. Yinyin is the Head of Data Science for the Artificial Intelligence Products Group at Intel. Yinyin works with a team of data scientists on applying deep learning and Intel Nervana technologies into business applications across different industry domains.
Principal Architect of Baidu, Tech Lead of PaddlePaddle, Chair of Baidu NLP Technical Committee. He joined Baidu in 2008 after graduating from Peking University. He has been working on natural language processing and deep learning. From 2009 to 2011, he served as key member in developing and launching the first machine learning ranking system of Baidu Search Engine. Then he has been in charge of the research and development of several technical fields, such as machine learning, semantic computing, language understanding and dialogue interaction. Now he is responsible for Baidu Deep Learning Platform as the chair of the technical oversight committee. He win the best Baidu employee award in 2012 and led the team to won the highest prize(million-dollar prize) of Baidu in 2015.
Dr. Song Jiqiang is the managing director of Intel Lab China. His research interests include: interaction technologies in smart robots, smart devices innovations of multiple forms, mobile multimedia computing, mobile platform performance optimization, new human-machine interface, and the creation of software and hardware environment for new application use.
Dr. Song joined Intel Lab China in 2008 as an application R&D director at the Tsinghua-Intel Advanced Mobile Technology Center, and is a core member of the team in developing the Intel Edison prototype. After Edison was successfully commercialized, he drove the development of the developer tools for Edison-based smart devices to promote the adoption of Edison among the maker community. He also invented a new device category called interactive porcelain. He is now committed to developing a smart service robot platform based on Intel’s technologies.
From 2001 to 2008, he was a post-doctoral researcher at The Chinese University of Hong Kong, the principal engineer at the Hong Kong Applied Science & Technology Research Institute (ASTRI), and the multimedia R&D director at Beijing SimpLight Nano Electronics Co., Ltd. In 2003, he won first place in the IAPR GREC International Circular Arc Recognition Algorithm Competition. Furthermore, in 2006, the computer graphics reading technology research in which he participated (and was the second author of) won second place in the "college of science and technology" award category, which was administered by the Ministry of Education. He is a senior member of the IEEE and CCF, and published over 40 papers at international journals and conferences such as IEEE TPAMI, IEEE TCSVT, Pattern Recognition, CVPR and ICPR.
Dr. Song Jiqiang received a doctoral degree in computing from Nanjing University in 2001 and his doctoral dissertation was awarded a national outstanding doctoral dissertation.
Steve Thorne is the Director of Artificial Intelligence Sales in the Data Center Sales organization. Steve and his team are responsible for global sales of Intel AI products and solutions. He has 23 years of experience at Intel in leadership roles spanning sales, product marketing and technical enablement. Prior roles include Director of DCG Americas Sales, Technical Enabling Director in Taipei, Taiwan, and Product Line Manager for Xeon platforms. Steve has a BS/CS from Georgia Tech and is based in Santa Clara, CA.
Ananth Sankaranarayanan is the Senior Director in the AI Products Group at Intel, leading the team responsible for enabling customers and partners worldwide to build AI solutions that scale, and accelerating them on Intel based Platforms. Prior to his current role, Ananth led the Big Data Analytics and HPC Solutions Engineering teams. He has been with Intel since 2001 in various engineering leadership roles, won the highest Intel recognition for delivering first production High Performance Computing capability. He holds 2 patents, has authored several technical publications, and recently co-authored a book chapter in "Artificial Intelligence for Autonomous Networks".
Neta Zmora is a deep learning research engineer at the Intel AI Lab, where he wrote Distiller, an open source Python package for neural network compression research. Previously, Neta was the lead software architect of Intel's Computer Vision Group DL software stack.
Anna Bethke is Intel AI's Head of AI for Social Good where she is leading and coordinating research surrounding the design of fair, transparent, and accessible AI systems. In addition, she has been establishing partnerships with social good organizations, enabling their missions with Intel's technologies and AI expertise. In her previous role as a deep learning data scientist she was a member of the Intel AI Lab, developing deep learning NLP algorithms as part of the NLP Architect open source repository. Anna received an M.S and B.S. in Aerospace Engineering from MIT in 2009 and 2007 respectively and previously worked as a geospatial data scientist at MIT Lincoln Labs and Argonne National Labs, and a senior data scientist at Lab41.
Nicholas della Cioppa is the Product Marketing Lead for AI Software in Intel's AI Products Group. Beginning his career as an economic consultant, Nicholas has nearly 10 years of experience applying analytics and big data to real world problems. Prior to joining Intel, Nicholas focused on applied economic research and later worked at multiple US-based startups holding a variety of roles ranging from strategy to operations and marketing. Nicholas holds a MA in Economics from Duke University, and a MBA from The University of Chicago Booth School of Business.
Sanping Li, Principal Research Scientist @ DELLEMC CTO TRIGr Graduated at the University of Massachusetts Lowell with Ph.D in computer engineering. Currently focusing on R&D in automated machine learning and meta-learning in ML workflow and data management; Spiking Neural Networks; deep learning harness offering the capability of quick & easy building, tuning models and automatic cross-node deployment.
Paul Rietze is currently leading OEM, LOEM, and ODM Business Development efforts for Intel's Artificial Intelligence Products Group (AIPG). He is responsible for initiating, evaluating and managing AI-focused strategic business opportunities, partnerships and alliances. His goals are to launch and ramp AIPG HW & SW product lines through OEMs and to drive market adoption by developing and executing global go-to-market plans
Jian Cheng is currently deputy director of AiRiA, director of AI&AC Joint Lab of Institute of Automation, Chinese Academy of Sciences. He received the B.S. and M.S. degrees in Mathematics from Wuhan University in 1998 and in 2001, respectively. In 2004, he got his Ph.D degree from Institute of Automation, Chinese Academy of Sciences. His current research interests include deep learning, AI chip design, image and video analysis, etc. He has authored or co-authored more than 100 academic papers and two books. He was the recipient of LU JIAXi Young Talent award, the first class prize of Natural Sciences, Chinese Institute of Electronics, the second class prize of natural sciences, Ministry of Education. He serves as associate editor of Pattern Recognition Journal, and Program co-chair of ACM International Conference on Internet Multimedia Computing and Services (ICIMCS'10), organizing chair of HHME 2010, publication chair of CCPR 2012.
Yongtao Wang is an Associate Professor of the Institute of Computer Science and Technology (ICST), Peking University, Beijing, China. He received his Ph.D. degree (2009) from Huazhong University of Science and Technology (HUST), Wuhan, China. He was a Research Scientist (2010-2011) at the Temasek Laboratories, Nanyang Technological University, Singapore. His current research interests are document image understanding, computer vision, and deep learning. He is the Principal Invesitgator of many research projects, funded by the National Natural Science Foundation of China, Beijing Natural Science Foundation, Alibaba AI Lab, etc. He has published more than 30 papers on top-tiered journals (such as IEEE Transaction on Image Processing, Pattern Recognition) and flagship conferences (such as ICCV, AAAI, MM, to name a few). His research team has won multiple awards in several object detection competitions on top international conferences (such as WAD on CVPR 2018, and VisDrone2018 on ECCV 2018) and scene text detection and recognition competitions (RCTW-17 and COCO-Text on ICDAR 2017).
Fei Qiao is currently the group leader of iVip (integrated Vision, intelligent perception) lab of Tsinghua University. His research interests are low power CMOS circuits design for multimedia sensor network, integrated intelligent visual processing algorithms and application systems, as well as heterogeneous integrated systems for smart perception systems for edge computing and robotics. Based on current smart camera hardware platform, Fei's group is focusing on increasing the energy-efficiency of the visual processing dramatically by some work of architectures level and circuits level, including approximate computing, physical computing and reconfigurable computing. Fei's works are supported by some national academic research projects, such as NSFC, and his group also has closer cooperation with some known industrial companies, such as Intel, ADI, IBM, NXP, Huawei and Xilinx. Fei's group has published about 90 conference and journal papers, including ISSCC, DAC, ICASSP, ICCE, PATMOS, ISQED and ISCAS. Additionally, the iVip group have been granted for about 30 patents. Fei is currently Publicity subcommittee chair of VSPC TC of IEEE CAS Society since 2016, and the Election subcommittee chair of DISPS TC of IEEE SP Society since 2018.
Dr. Tao Jianhua is currently the Professor and Deputy Director in National Laboratory of Pattern Recognition (NLPR), the Assistant President of Institute of Automation (CASIA) of Chinese Academy of Sciences, the Principal Professor and the Assistant Dean of Artificial Intelligence College, the University of Chinese Academy of Sciences. He also works as the Deputy Director of Artificial Intelligence Working Group of the Standardization Administration of the People's Republic of China, the Council Member of Chinese Association for Artificial Intelligence (CAAI).
His research interests include speech recognition and synthesis, human computer interaction, emotional information processing. He has published more than 300 papers in major journals and proceedings, such as IEEE TASLP, IEEE TIP, Speech Communication, Interspeech, ICASSP, ICCV, ICIP, ICME, ICPR, ACII, etc. He received several awards from important conferences including Eurospeech2001.
Dr. Tao also serves as the editor of a number of major domestic and foreign journals, as well as the chairman of conferences and conferences, such as Interspeech, ACII, IEEE ICSP, IEEE MLSP, ISCSLP, NCMMSC and so on.
Dr. Tao received his M.S. degree from Nanjing University in 1996 and the Ph.D. degree in Computer Science from Tsinghua University in 2001.
Wenwu Zhu currently serves as EiC for IEEE Transaction for Multimedia. He served as Guest Editors for the Proceedings of the IEEE, IEEE Journal on Selected Areas in Communications, ACM Transactions on Intelligent Systems and Technology, etc.; and Associate Editors for IEEE Transactions on Mobile Computing, ACM Transactions on Multimedia, IEEE Transactions on Circuits and Systems for Video Technology, and IEEE Transactions on Big Data, etc. He served in the steering committee for IEEE Transactions on Multimedia (2015-2016) and IEEE Transactions on Mobile Computing (2007-2010), respectively. He served as TPC Co-chair for ACM Multimedia 2014 and IEEE ISCAS 2013, respectively. He serves as General Co-Chair for ACM Multimedia 2018 and ACM CIKM 2019, respectively.
Zhou, Haiwei is currently an architect of the iQiyi deep learning cloud. Haiwei had experience in embedded Java porting and performance optimization. Haiwei is focusing on design and integration of the iQiyi deep learning cloud. He interests to container cloud, high performance computing and deep learning.
Yurong Chen, Ph.D., Principle Research Scientist at Intel Corporation and Director of Cognitive Computing Lab at Intel Labs China. Dr. Chen is responsible for driving cognitive computing, especially visual cognition including visual analytics and understanding, and machine learning research for Intel next-generation platforms. He is also the co-owner of Intel Labs "Visual Understanding and Synthesis" program, driving research innovation in smart visual data processing technologies on Intel platforms across Intel Labs.
Zhang Yunxing, the artificial intelligence R&D manager of AccuRad Healthcare Network Co.,Ltd. Xi'an, and has long been engaged in image processing and machine learning. After joining AccuRad Healthcare Network Co.,Ltd. Xi'an, he applies deep learning technology to medical image processing, and hosts and develops a variety of single-patient auxiliary diagnostic systems.
Wei Yu is working as an AI architect in Intel Data Center AI Sales and Technical support team of AsiaPac and PRC region. He received his Ph.D in EEE from NTU, Singapore. He has been working in image processing, pattern recognition, machine learning and deep learning for many years. He has supported many AI applications in different industries.
UMCloud CTO, Ph.D. in Computer Science from George Mason University and M.S. from Peking University, expert in distributed computing, large-scale machine learning, massive data processing, and served as a data platform architect for Google's advertising department.
Joined Intel for 3 years providing technical consulting and enabling supports for Intel® AI software solutions, including Intel® MKL/MKL-DNN, Intel® OpenVINO™ , and Intel Performance libraries (IPP/MKL/DAAL) to Intel worldwide strategic customers, enabling Intel internal and external customers to be successful with Intel platform through use Intel Software Technology and Products. Has rich experience of Computer Vision and Deep Learning inference from Edge to cloud computing and be proficient in deep learning frameworks and Neural Network models optimization and performance tuning base on Intel Architecture platform.
Senior Engineering Manager, Data Analytics Technology of Intel
Jiang Yu is currently a big data specialist in Alibaba Cloud EMR. His works focused on Hadoop Kernel development, and machine learning, deep learning integration into big data platform.
Wei-Wei Tu, senior machine learning architect in 4Paradigm. He has rich experience in architecting large-scale distributed machine learning system, designing and applying large-scale machine learning algorithms, and designing online marketing systems. He used to work on Baidu's Pheonix Nest Project, focusing on ad click-through-rate prediction, and he designed and developed Baidu's large-scale distributed machine learning computation framework - ELF. At 4Paradigm, Wei-Wei Tu works as the designer of GDBT, the large-scale distributed machine learning computation framework of 4Paradigm's Prophet platform, and applies AutoML and transfer learning to many industrial applications with significant improvement. Wei-Wei Tu is also the main organizer of the NIPS 2018 AutoML competition, the chair of the PAKDD 2018/2019 competition, and the chair of the PRICAI 2018 AutoML Workshop.
Sheng Fu, Senior software engineer in Intel AIPG Tensorflow Direct Optimization team. He has been leading the optimizing for various HPC related software on IA, including machine learning, computer graphics, and life science.
Wenzhe Xue is a deep learning Engineer and leads nGraph distributed architecture in Algorithm team, AIPG, Intel. He did medical image analysis research at Mayo Clinic, Arizon and obtained PhD from Biomedical Informatics Department at Arizona State University. He obtained his BS degree on Telecommunication Engineering from University of Electronic Science and Technology of China.
Joined Intel Movidius China AE team in July 2017, before that worked in TI for 9 years, ZTE for 4 years. Expertise in DSP programming and algorithm optimization. Currently focus on MyriadX shave and NCE programming and optimization.
Robin is working with Intel Programmable Solutions Group (PSG) AsiaPac and PRC region, as managing the FPGA new business developments in the data centers segment and the virtualization and the AI tech areas. He appreciates almost 20 years FPGA application experiences and expertise, with about 5 recent years for acceleration technologies and businesses.
Yao Matrix is a senior Cloud AI software architect in Intel. Matrix has 6 years AI algorithm and solution experience. Before Intel, Matrix worked for Cheetah in recommendation systems and Baidu in dynamical positioning, both as technical lead. In Intel, Matrix mainly focus on landing Intel AI solutions into China tier-1 CSP customers' product. Matrix holds 5 patents.
Harvey (He, Ke) received PhD Degree of Electronic Electrical Engineer from University of Strathclyde, Glasgow, UK in 2012. Currently he is working as Platform Solution Architecture at IoTG Intel, mainly focusing on Intel Hetero. Platforms (FPGA/VPUs) Acceleration for Deep Learning-based Video Use Scenarios. His interests including Hetero. Architecture and Optimization (FPGA/VPUs), Deep Learning Network Enabling and Optimization on Hetero. Platforms, and Pipeline or System Optimization
Xu is the Director of ISV Partnerships, AIPG at Intel, responsible for managing the ISV partnerships within the Intel AI Builders program. Before his current role, Xu was an Investment Director at Intel Capital, focusing on equity investments in AI and FPGA opportunities. Prior to that, Xu was a portfolio manager supporting Intel Capital investment portfolio companies in China and APAC region. Xu holds MFE from University of California, Berkeley and is a CFA charter holder.
The above information will be continuously updated.
The final information will be finalised during the actual event day.
|PRESENTER||POSTER TITLE IN ENGLISH|
|Anbang Yao (ILC)||Efficient Semantic Scene Completion Network with Spatial Group Convolution|
|Yiwen Guo (ILC)||Deep Defense: Training DNNs with Improved Adversarial Robustness (to appear in NIPS'18)|
|Xiaofan Xu (Movidius)||Hybrid Pruning: Thinner Sparse Networks for Fast Inference on Edge Devices|
|Haim Barad (Intel AIPG Inference Group)||Early Exit for Fast Inference Classification|
|Karthik Vadla, G Anthony Reina (AI Lab)||HOROVOD DISTRIBUTED TRAINING ON K8S USING MLT|
|Yinpeng Dong (THU)||Boosting Adversarial Attacks with Momentum|
|Chenyang Si (CASIA)||Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning|
|Bo Peng (CASIA)||Position Determines Perspective: Investigating Perspective Distortion for Image Forensics of Faces|
|Jiangyan Yi (CASIA)||Convolutional Interaction Network for Natural Language Inference|
|Jingjing Gong (Fudan)||Convolutional Interaction Network fo Natural Language Inference|
|Youmeng Li (TJU)||A Hydrology Simulation Method Using LSTM|
|Shuai Gao (USTC)||SATB-Nets:Training Deep Neural Networks with Segmented Asymmetric Ternary and Binary Weights|
|Jun Shi (USTC)||DSU-Net: Cervical Cancer CTV Automatic Labelling Method Based on Convolutional Neural Network|
|Zhicheng He (NankaiU)||Content to Node: Self-Translation Network Embedding|
|Wei Feng (TJU)||Active Camera Relocalization from a Single Reference Image without Hand-Eye Calibration|
|Xin Yang (DLUT)||Active Matting|
|Yihua Huang (NanjingU)||Reinforcement Learning-Based Automatic Machine Learning (AutoML) Algorithm & Framework|
|Hao Wang (RenminU)||Laser Scar Detection in Fundus Images using Convolutional Neural Networks|
|Sanyuan Zhao||Pyramid Dilated Deeper ConvLSTM for Video Salient Object Detection|
|Li Jiang (SHJ)||ReCom: An Efficient Resistive Accelerator for Compressed Deep Neural Networks|
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