Spring School/Workshop in Montreal: LAST ANNOUNCEMENT

Thu Mar 28 09:08:04 EST 1996

******** Last reminder, there are only a few seats left: *******

        Montreal Workshop and Spring School on
Artificial Neural Networks and Learning Algorithms

                 April 15-30 1996 

Centre de Recherche Mathematique, Universite de Montreal

This workshop and concentrated course on artificial neural networks and
learning algorithms is organized by the Centre de Recherches
Mathematiques of the University of Montreal (Montreal, Quebec, Canada).
The first week of the the workshop will concentrate on learning theory,
statistics, and generalization. The second week (and beginning of third) will
concentrate on learning algorithms, architectures, applications and
implementations. 

The organizers of the workshop are Bernard Goulard (Montreal), Yoshua
Bengio (Montreal), Bertrand Giraud (CEA Saclay, France) and Renato De
Mori (McGill). 

The invited speakers are G. Hinton (Toronto), V. Vapnik (AT&T), M. Jordan
(MIT), H. Bourlard (Mons), T. Hastie (Stanford), R. Tibshirani (Toronto), F.
Girosi (MIT), M. Mozer (Boulder), J.P. Nadal (ENS, Paris), Y. Le Cun
(AT&T), M. Marchand (U of Ottawa), J. Shawe-Taylor (London), L. Bottou
(Paris), F. Pineda (Baltimore), J. Moody (Oregon), S. Bengio (INRS
Montreal), J. Cloutier (Montreal), S. Haykin (McMaster), M. Gori
(Florence), J. Pollack (Brandeis), S. Becker (McMaster), Y. Bengio
(Montreal), S. Nowlan (Motorola), P. Simard (AT&T), G. Dreyfus (ESPCI
Paris), P. Dayan (MIT), N. Intrator (Tel Aviv), B. Giraud (France), H.P.
Graf (AT&T). 

MORE INFO AT: http://www.iro.umontreal.ca/labs/neuro/spring96/english.html
OR contact Louis Pelletier, pelletl at crm.umontreal.ca, #tel: 514-343-2197 

--------------------  SCHEDULE ---------------------------------

The lectures will take place in room 5340 (5th floor) of the Pavillon
Andre-Aisenstadt on the campus of the Universite de Montreal. 

Week 1 
Introduction, learning theory and statistics 

April 15: 

       9:00 - 9:30 Registration (Room 5341) & Coffee (Room 4361) 
       9:30 - 10:30 Y. Bengio: Introduction to learning theory and learning
       algorithms 
       10:30 - 11:30 J.P. Nadal: Constructive learning algorithms: empirical
       study of learning curves (part I) 
       14:00 - 15:00 G. Dreyfus: Learning to be a dynamical system (part I)
       15:00 - 15:30 B. Giraud: Flexibility, robustness and algebraic
       convenience of neural nets with neurons having a window-like
       response function 

April 16: 

       9:00 - 10:00 Y. Bengio: Introduction to artificial neural networks and
       pattern recognition 
       10:00 - 11:00 F. Girosi: Neural networks and approximation theory
       (part I) 
       11:00 - 11:30 Coffee Break (room 4361) 
       11:30 - 12:30 L. Bottou: Learning theory for local algorithms 
       14:00 - 15:00 J.P. Nadal: Constructive learning algorithms: empirical
       study of learning curves (part II) 
       15:00 - 16:00 G. Dreyfus: Learning to be a dynamical system (part
       II) 

April 17: 

       9:00 - 10:00 V. Vapnik: Theory of consistency of learning processes 
       10:00 - 11:00 L. Bottou: Stochastic gradient descent learning and
       generalization 
       11:00 - 11:30 Coffee Break (room 4361) 
       11:30 - 12:30 F. Girosi: Neural networks and approximation theory
       (part II) 
       14:00 - 15:00 M. Marchand: Statistical methods for learning
       nonoverlapping neural networks (part I) 
       15:00 - 16:00 J. Shawe-Taylor: A Framework for Structural Risk
       Minimisation (part I) 
       16:00 - 16:30 Coffee Break (room 4361) 
       16:30 - 17:30 M. Jordan: Introduction to Graphical Models 

April 18: 

       9:00 - 10:00 J. Shawe-Taylor: A Framework for Structural Risk
       Minimisation (part II) 
       10:00 - 11:00 R. Tibshirani: Regression shrinkage and selection via
       the lasso 
       11:00 - 11:30 Coffee Break (room 4361) 
       11:30 - 12:30 V. Vapnik: Non-asymptotic bounds on the rate of
       convergence of learning processes 
       14:00 - 15:00 T. Hastie: Flexible Methods for Classification (part I) 
       15:00 - 16:00 M. Jordan: Algorithms for Learning and Inference in
       Graphical Models 

April 19: 

       9:00 - 10:00 S. Bengio: Introduction to Hidden Markov Models 
       10:00 - 11:00 V. Vapnik: The learning algorithms 
       11:00 - 11:30 Coffee Break (room 4361) 
       11:30 - 12:30 R. Tibshirani: Model search and inference by bootstrap
       "bumping" 
       14:00 - 15:00 T. Hastie: Flexible Methods for Classification (part II)
       15:00 - 16:00 M. Marchand: Statistical methods for learning
       nonoverlapping neural networks (part II) 
       16:00 - 16:30 Coffee Break (room 4361) 
       16:30 - 17:30 B. Giraud: Spectrum recognition via the
       pseudo-inverse method and optimal background subtraction 

Week 2 and 3 
Algorithms, architectures and applications 

April 22: 

       9:00 - 9:30 Registration (room 5341) & Coffee (room 4361) 
       9:30 - 10:30 S. Haykin: Neurosignal Processing: A Pradigm Shift in
       Statistical Signal Processing (part I) 
       10:30 - 11:30 H. Bourlard: Using Markov Models and Artificial
       Neural Networks for Speech Recognition (part I) 
       14:00 - 15:00 M. Gori: Links between suspiciousness and
       computational complexity 
       15:00 - 16:00 M. Mozer: Modeling time series with compositional
       structure 
       16:00 - 16:30 Coffee Break (room 4361) 
       16:30 - 17:30 F. Pineda: Reinforcement learning and TD-lambda 

April 23: 

       9:00 - 10:00 S. Haykin: Neurosignal Processing: A Pradigm Shift in
       Statistical Signal Processing (part II) 
       10:00 - 11:00 F. Pineda: Hardware architecture for acoustic transient
       classification 
       11:00 - 11:30 Coffee Break (room 4361) 
       11:30 - 12:30 H. Bourlard: Using Markov Models and Artificial
       Neural Networks for Speech Recognition (part I) 
       14:00 - 15:00 J. Pollack: 
       15:00 - 16:00 P. Dayan: Factor Analysis and the Helmholtz Machine
       Dynamical properties of networks for cognition 
       16:00 - 16:30 Coffee Break (room 4361) 
       16:30 - 17:30 M. Mozer: Symbolically-Constrained Subsymbolic
       Processing 

April 24: 

       9:00 - 10:00 M. Gori: Number-plate recognition with neural
       networks 
       10:00 - 11:00 J. Pollack: A co-evolutionary framework for learning 
       11:00 - 11:30 Coffee Break (room 4361) 
       11:30 - 12:30 P. Dayan: Bias and Variance in TD Learning 
       14:00 - 15:00 S. Becker: Unsupervised learning and vision (part I) 
       15:00 - 16:00 P. Simard: Memory-based pattern recognition 

April 25: 

       9:00 - 10:00 S. Becker: Unsupervised learning and vision (part I) 
       10:00 - 11:00 G. Hinton: Improving generalisation by using noisy
       weights 
       11:00 - 11:30 Coffee Break (room 4361) 
       11:30 - 12:30 N. Intrator: General methods for training ensembles of
       regressors (part I) 
       14:00 - 15:00 S. Nowlan: Mixtures of experts 
       15:00 - 16:00 G. Hinton: Helmholtz machines 
       16:00 - 16:30 Coffee Break (room 4361) 
       16:30 - 17:30 Y. Le Cun: Shape Recognition with Gradient-Based
       Learning Methods 

April 26: 

       9:00 - 10:00 S. Bengio: Input/Output Hidden Markov Models 
       10:00 - 11:00 Y. Le Cun: Fast Neural Net Learning and Non-Linear
       Optimization 
       11:00 - 11:30 Coffee Break (room 4361) 
       11:30 - 12:30 S. Nowlan: Mixture of experts to understand functional
       aspects of primate cortical vision 
       14:00 - 15:00 N. Intrator: General methods for training ensembles of
       regressors (part II) 
       15:00 - 16:00 P. Simard: Pattern Recognition Using a Transformation
       Invariant Metric 

April 29: 

       9:00 - 10:00 J. Moody: Artificial Neural Networks applied to finance
       (part I) 
       10:00 - 11:00 Y. Bengio: Modeling multiple time scales and training
       with a specialized financial criterion 
       14:00 - 15:00 H.P. Graf: Recent Developments in Neural Net
       Hardware (part I) 

April 30: 

       9:00 - 10:00 J. Moody: Artificial Neural Networks applied to finance
       (part II) 
       10:00 - 10:30 Coffee Break (room 4361) 
       10:30 - 11:30 H.P. Graf: Recent Developments in Neural Net
       Hardware (part II) 
       14:00 - 15:00 J. Cloutier:FPGA-based multiprocessor:
       Implementation of Hardware-Friendly Algorithms for Neural
       Networks and Image Processing 
       15:00 - 15:30 Coffee Break (room 4361) 
       15:30 - 16:30 J. Moody: Artificial Neural Networks applied to finance
       (part III) 

-------------------- Registration information: ---------------------

       $100 (Canadian) or $75 (US) if received before April 1st 
       $150 (Canadian) or $115 (US) if received on or after April 1st 
       $25 (Canadian) or $19 (US) for students and post-doctoral fellows. 

The number of participants will be limited, on a first-come first-served
basis. Please register early! 

Registration forms and hotel informations are available at our WEB SITE:

  http://www.iro.umontreal.ca/labs/neuro/spring96/english.html

For more information, contact Louis Pelletier, pelletl at crm.umontreal.ca
514-343-2197 

Centre de Recherche Mathematique, Universite de Montreal, C.P. 6128,
Succ. Centre-Ville, Montreal, Quebec, H3C-3J7, Canada. 

-- 
Yoshua Bengio 
Professeur Adjoint, Dept. Informatique et Recherche Operationnelle
Pavillon Andre-Aisenstadt #3339 , Universite de Montreal, 
Dept. IRO, CP 6128, Succ. Centre-Ville,
2920 Chemin de la tour, Montreal, Quebec, Canada, H3C 3J7

E-mail: bengioy at iro.umontreal.ca      Fax:       (514) 343-5834
web: http://www.iro.umontreal.ca/htbin/userinfo/user?bengioy
or http://www.iro.umontreal.ca/labs/neuro/
Tel: (514) 343-6804. Residence: (514) 738-6206