|9.00 - 11.00||J.-C. Prevotet (Univ. P. et M. Curie, Paris) "Neural Networks: Architectures, preprocessing and hardware implementation", slides (PPT)|
|11.00 - 11.30||Coffee-break|
|11.30 - 13.30||S. Terekhoff (NeurOK, Moscow) "Neural approximations of probability density in informational modeling", slides (PS) in Russian (some slides in English on "Data Mining" topic, from 15th page).|
|13.30 - 15.00||Lunch|
|15.00 - 17.00||S. Shumsky (NeurOK, Moscow) "Bayesian regularization of learning", slides (PPT)|
|Tutorial titles||Lecturer(s)||Dates, subtitles and duration of the tutorials||Comments|
|Neural Networks: Architectures, preprocessing and hardware implementation||J.-C. Prevotet||23 June (2 hours)||
The objectives of the tutorial are to present general neural
networks concepts; introduce some classical neural architectures,
(Multi-Layer Perceptrons, Radial Basis Function, Time Delay Neural
stress the importance of preprocessing; and provide a survey of neural
network hardware techniques. Applications in which neural networks have
successfully demonstrated their superiority over other classical methods
and results will be shown.
An important aspect of neural networks resides in the nature of data provided as inputs. Pre-processing concepts will be introduced and it will be shown to what extent it is possible to model input data to provide more computational power to neural networks.
Hardware aspects will be discussed and general architectures utilized to simulate neural networks in real time presented. We will focus on specific applications, which are highly constrained in terms of execution speed and quantity of data to be processed. An overview of the hardware solutions for this case will be outlined, and some commercial devices as well as academic circuits shown.
An overview of current technology and an introduction to programmable logic will then be given, explaining in what way it might substitute for custom devices. Finally, new technological challenges as well as the future of neuro-hardware will be discussed.
|Neural approximations of probability density in informational modeling||S. Terekhoff||23 June (2 hours)||The problem of probability density approximation, based on set of multivariate experimental data is considered from the point of view of practical informatics. Effective neural techniques of approximation of the density form are proposed. Statements of several data analysis problems are presented using density approximation approach. Applications of the method are discussed.|
|Bayesian regularization of learning||S. Shumsky||23 June (2 hours)||
Bayesian approach based on the first principles of probability
theory is the most consistent paradigm of statistical learning. From
practical perspective Bayesian learning offers intrinsic regularization
procedure providing a viable alternative to traditional cross-validation
This lecture provides both theoretical background for Bayessian learning (in particular in relation to Minimal Description Length and Maximum Entropy principles) and its practical applications to noisy measurements, neural networks learning and clustering.