2014년도 대한전자공학회 하계종합학술대회

 

Plenary Speech 소개

Deep Learning RNNaissance
Professor Jürgen Schmidhuber
Artificial Intelligence (AI), Scientific Director of the Swiss AI Lab IDSIA
 
Machine learning and pattern recognition are currently being revolutionised by "Deep Learning" (DL) Neural Networks (NNs). This is of commercial interest, e.g., Google spent over 400m US$ on start-up *DEEPMIND co-founded by our student. My talk will summarize our work on DL since 1991.Our recurrent NNs (RNNs) were the first to win official international competitions in pattern recognition and machine learning. So far our team has won nine contests, more than any other research group or company. We also built the first artificial RNN-based agent that learns from scratch complex control based on high-dimensional vision. Time permitting, I'll also address curious and creative machines and theoretically optimal, universal, self-modifying artificial intelligences.
 
* http://www.androidauthority.com/google-deepmind-acquisition-339360/
 
  
Bio: Jürgen Schmidhuber, Professor of Artificial Intelligence (AI), Scientific Director of the Swiss AI Lab IDSIA (a Business Week Top 10 AI Lab), recipient of the 2013 Helmholtz Award, has led a team that won many international competitions and awards in the fields of machine learning & AI, established the field of optimal universal problem solvers, and formally explained fun & subjective beauty & creativity & curiosity & art & science. His lab's deep learning artificial neural networks were the first methods to achieve human-competitive performance on important computer vision benchmarks; many famous companies are now using them.
 
http://www.idsia.ch/~juergen/

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Smart Sensors on the IT Platform
Chong-Min Kyung
CISS and KAIST 

 
 
Smart sensor is considered to be one of the next market driver after smart phone. Design process of smart sensors is very complicated and needs to be managed in an holistic fashion, and throughout its life cycle, i.e., from device, circuit to systems applications such that an appropriate objective function consisting of a set of figure of merits such as speed, error allowance, energy consumption, and cost is minimized. This talk covers the motivation/vision, planning and validation, project architecture, directions of operation/management support, and future prospect of smart sensor research in CISS (Center for Integrated Smart Sensors) launched in KAIST as a government-funded Global Frontier Project in 2011. Some examples of research projects such as gas, water, radioactivity, structural health monitoring, surveillance camera, black box camera, brain activity sensing, endoscopy will be explained.
 
 
 
Bio: Chong-Min Kyung received B.S. in EE from Seoul National University in 1975, M.S. and Ph.D. in EE from KAIST in 1977 and 1981, respectively. He joined KAIST in 1983, and has been working on CAD, computer graphics, and System-on-a-Chip design including  RISC/CISC microprocessors, VLIW and reconfigurable DSP cores.
His current research focuses on applying system-level cost-energy-rate-distortion optimization techniques to the design of smart sensors, especially smart cameras. He currently leads Center for Integrated Smart Sensors, a Global Frontier Project supported by Korean Government.
He received numerous awards in international conferences including ASP-DAC 1997 and 1998, Best Paper Awards in DAC in 2000, ICSPAT in 1999, ICCD in 1999, and ISQED in 2014. He is a member of National Academy of Engineering Korea, and Korean Academy of Science and Technology, and IEEE fellow.
http://vswww.kaist.ac.kr/








 

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