Deep architectures are important for machine learning, for engineering applications, and for understanding the brain. We will first provide a brief historical overview of deep architectures from their 1950s origins to today. Motivated by this overview, we will study and prove several theorems regarding deep architectures and one of their main ingredients--autoencoder circuits--in particular in the unrestricted Boolean and unrestricted probabilistic cases. We will show how these analyses lead to a new family of learning algorithms for deep architectures--the deep target (DT) algorithms. The DT approach converts the problem of learning a deep architecture into the problem of learning many shallow architectures by providing learning targets for each deep layer. Finally, we will present applications of deep architectures and deep learning to problems in computational biology.
Pierre Baldi is Chancellor’s Professor in the Department of Computer Science and Director of the Institute for Genomics and Bioinformatics 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 over 260 peer-reviewed research articles and four books. He is the recipient of the 1993 Lew Allen Award, the 1999 Laurel Wilkening Faculty Innovation Award, a 2006 Microsoft Research Award, and the 2010 E.R. Caianiello Prize for research in machine learning. He is Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronics Engineers (IEEE).
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