第17回統計的機械学習セミナー / The 17th Statistical Machine Learning Seminar
- 16 May, 2014 (Fri) 15：00－
- Seminar Room 5（D313）@ Institute of Statistical Mathematics
- Deep Learning: Theory, Algorithms, and Applications
- Professor Pierre Baldi (University of California, Irvine)
- Learning is essential for building intelligent systems, whether carbon-based or silicon-based ones.
Moreover these systems do not solve complex tasks in a single step but rather go through multiple processing stages.
Hence the question of deep learning, how efficient learning can be implemented in deep architectures.
This fundamental question not only impinges on problems of memory and intelligence in the brain, but it is also at the forefront of current machine learning research. In the last year alone, new performance breakthroughs have been achieved by deep learning methods in applications areas ranging from computer vision, to speech recognition, to natural language understanding, to bioinformatics. This talk will provide a brief overview of deep learning, from its biological origins to some of the latest theoretical, algorithmic, and application results. Particular emphasis will be given to the mathematical analysis of the dropout algorithm, a relatively new randomization algorithm for deep learning, and the development of learning methods–in the form of recursive neural networks– for structured, variable-size, data, and their applications to the problems of predicting the properties of small molecules and the structure of proteins.
- Short Bio
- Pierre Baldi is Chancellor’s Professor in the Department of Computer Science, Director of the Institute for Genomics and Bioinformatics, and Associate Director of the Center for Machine Learning and Intelligent Systems at the University of California, Irvine. He received his PHD degree from the California Institute of Technology. His research work is at the interface of the computational and life sciences, in particular the application of artificial intelligence and statistical machine learning methods to problems in chemoinformatics, genomics, systems biology, and computational neuroscience. He is credited with pioneering the use of Hidden Markov Models (HMMs), graphical models, and recursive neural networks in bioinformatics.
Dr. Baldi has published four books and over 250 peer-reviewed research articles with an H-index of 68. He is the recipient of the 1993 Lew Allen Award at JPL, the 1999 Laurel Wilkening Faculty Innovation Award at UCI, a 2006 Microsoft Research Award, and the 2010 E. R. Caianiello Prize for research in machine learning.
He is also am elected Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB.